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Description: Summarization Papers. Contribute to xcfcode/Summarization-Papers development by creating an account on GitHub.

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Summarization Learning Route (with link)http://xcfeng.net/res/summarization-route.pdf
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Dialogue Summarization (2022.1)https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers/blob/main/slides/presentation/Dialogue_Summarization_DAMO.pdf
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如何把DialoGPT用到对话摘要任务？@ ACL 2021https://mp.weixin.qq.com/s/GQQRRS3F7p4Zv6wSuDh0ng
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会议摘要有难度？快来引入对话篇章结构信息https://mp.weixin.qq.com/s/Be7AYUPdux8NvAO4wo6_fg
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文本摘要简述 (Chinese)https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers/blob/main/slides/presentation/%E6%96%87%E6%9C%AC%E6%91%98%E8%A6%81%E7%AE%80%E8%BF%B0.pdf
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https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#big-model-era
[pdf]https://arxiv.org/abs/2305.01951
[data]https://github.com/AndyCheang/TempoSum
[pdf]https://arxiv.org/abs/2305.01146
[pdf]https://arxiv.org/abs/2304.08448
[pdf]https://arxiv.org/abs/2304.04193
[pdf]https://arxiv.org/abs/2304.02554
[pdf]https://arxiv.org/abs/2302.14229
[pdf]https://arxiv.org/abs/2302.08081
[pdf]https://arxiv.org/abs/2209.12356
[code]https://tagoyal.github.io/zeroshot-news-annotations.html
[pdf]https://arxiv.org/abs/2301.13848
[pdf]https://arxiv.org/abs/2302.06476
[pdf]https://arxiv.org/abs/2302.04023
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#decomposed
[pdf]https://arxiv.org/abs/2209.10492
[code]https://github.com/swarnaHub/SummarizationPrograms
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#benchmark
[pdf]https://arxiv.org/abs/2301.13848
[pdf]https://arxiv.org/abs/2202.07362
[data]https://github.com/ghomasHudson/muld
[pdf]http://explainaboard.nlpedia.ai/ExplainaBoard.pdf
[ExplainaBoard]http://explainaboard.nlpedia.ai/leaderboard/task-summ/index.php
[pdf]https://arxiv.org/abs/2011.11928
[benchmark]https://github.com/microsoft/glge
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#survey
[pdf]https://arxiv.org/abs/2304.08763
[code]https://github.com/zhehengluoK/Biomedical-Text-Summarization-Survey/tree/master
[pdf]https://arxiv.org/abs/2212.01669
[pdf]https://arxiv.org/abs/2210.09894
[pdf]https://aclanthology.org/2022.coling-1.536/
[pdf]https://arxiv.org/abs/2203.12515
[pdf]https://arxiv.org/abs/2207.00939
[pdf]https://dl.acm.org/doi/10.1145/3529754
[pdf]https://arxiv.org/abs/2204.11190
[pdf]https://arxiv.org/abs/2107.03175
[pdf]https://arxiv.org/abs/2204.01849
[pdf]https://arxiv.org/abs/2203.05227
[pdf]https://arxiv.org/abs/2203.03047
[pdf]https://arxiv.org/abs/2202.03629
[pdf]https://arxiv.org/abs/2202.01110
[pdf]https://arxiv.org/abs/2201.05337
[pdf]https://arxiv.org/abs/2201.05273
[pdf]https://arxiv.org/abs/2109.10118
[pdf]https://arxiv.org/abs/2109.05199
[pdf]https://arxiv.org/abs/2107.13586
[pdf]https://arxiv.org/abs/2105.10311
[pdf]https://arxiv.org/abs/2105.00824
[pdf]https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9328413
[pdf]https://www.sciencedirect.com/science/article/pii/S1319157820303554
[pdf]https://arxiv.org/abs/2011.04843
[pdf]https://www.semanticscholar.org/paper/Deep-Learning-Based-Abstractive-Text-Summarization%3A-Suleiman-Awajan/b7da726c244287748575ef404009609afde45bea
[pdf]https://arxiv.org/abs/2010.04389
[pdf]https://arxiv.org/abs/2005.04684
[pdf]https://arxiv.org/abs/1812.02303
[pdf]https://arxiv.org/abs/1804.04589
[pdf]https://arxiv.org/abs/1706.08162
[pdf]https://arxiv.org/abs/1704.03242
[pdf]https://arxiv.org/abs/1707.02268
[pdf]https://link.springer.com/article/10.1007/s10462-016-9475-9
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#toolkit
[pdf]https://arxiv.org/abs/2210.09587
[demo]https://tldr.demo.webis.de/summarize
[pdf]https://arxiv.org/abs/2109.11621
[demo]https://biu-nlp.github.io/iFACETSUM/WebApp/client/
[pdf]https://arxiv.org/abs/2108.12738
[Demo]https://github.com/Yale-LILY/SummerTime
[pdf]https://arxiv.org/abs/2108.01879
[web]https://tldr.webis.de/
[code]https://github.com/fastnlp/fastSum
[code]https://github.com/graph4ai/graph4nlp
[summarization]https://github.com/graph4ai/graph4nlp/tree/master/examples/pytorch/summarization
[pdf]https://arxiv.org/abs/2012.04281
[code]https://github.com/hyunwoongko/summarizers
[pdf]https://www.aclweb.org/anthology/W18-1817.pdf
[code]https://github.com/OpenNMT/OpenNMT-py
[code]https://github.com/pytorch/fairseq
[pdf]https://www.aclweb.org/anthology/N19-4012/
[code]https://github.com/tshi04/LeafNATS
[code]https://github.com/HHousen/TransformerSum
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#analysis
https://camo.githubusercontent.com/b4b8d220c41cc3ac4cbb3ef85d7c4bbdb5a11886bd646085a3161b17c66ad1ad/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f416e616c797369732d616e616c797369732d726564
https://camo.githubusercontent.com/bde0c9611477eb9f4b530c20da4cba30f4027361be4f551e38c7af1d6b9782d7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4d6574612532304576616c756174696f6e2d6576616c756174696f6e2d627269676874677265656e
https://camo.githubusercontent.com/450d3781ac1a8db4a365b55dcdad629678b87cc066059792a353f39c79964993/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f426961732d626961732d6f72616e6765
https://camo.githubusercontent.com/4bae9682af42aee662f0fe4ab554c5c45fc6dc9c531dd93e08aa6fc4cb57c11b/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4172636869746563747572652d6172636869746563747572652d626c7565
[pdf]https://aclanthology.org/2022.emnlp-main.694/
[code]https://github.com/raymondzmc/Attention-Pattern-Exploitation
[pdf]https://arxiv.org/abs/2210.14606
[pdf]https://arxiv.org/abs/2203.15721
https://arxiv.org/abs/2110.08370https://arxiv.org/abs/2110.08370
[pdf]https://arxiv.org/abs/2109.08129
[pdf]https://arxiv.org/abs/2106.11388
[code]https://github.com/priyamtejaswin/howwelldoyouknow
[pdf]https://aclanthology.org/2021.acl-long.539/
[code]https://github.com/jiacheng-xu/sum-interpret
[pdf]https://arxiv.org/abs/2106.01581
[code]https://github.com/mwilbz/pointer-generator-analysis
[pdf]https://arxiv.org/abs/2012.07619
[pdf]https://www.aclweb.org/anthology/2020.emnlp-main.649/
https://camo.githubusercontent.com/87b60393e5b05b455218f8c2f7b89fbb204061853de0b4ffe0e045cab47d0064/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d616e616c797369732d726564
[pdf]https://arxiv.org/abs/2011.04096
[code]https://github.com/manikbhandari/RevisitSummEvalMetrics
https://camo.githubusercontent.com/87b60393e5b05b455218f8c2f7b89fbb204061853de0b4ffe0e045cab47d0064/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d616e616c797369732d726564
[pdf]https://arxiv.org/abs/2004.02664
https://camo.githubusercontent.com/87b60393e5b05b455218f8c2f7b89fbb204061853de0b4ffe0e045cab47d0064/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d616e616c797369732d726564
[pdf]https://www.aclweb.org/anthology/2020.findings-emnlp.254/
https://camo.githubusercontent.com/87b60393e5b05b455218f8c2f7b89fbb204061853de0b4ffe0e045cab47d0064/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d616e616c797369732d726564
[pdf]https://arxiv.org/abs/2010.12495
[code]https://github.com/CogComp/content-analysis-experiments
https://camo.githubusercontent.com/87b60393e5b05b455218f8c2f7b89fbb204061853de0b4ffe0e045cab47d0064/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d616e616c797369732d726564
[pdf]https://arxiv.org/abs/2010.07882
[code]https://github.com/jiacheng-xu/text-sum-uncertainty
https://camo.githubusercontent.com/87b60393e5b05b455218f8c2f7b89fbb204061853de0b4ffe0e045cab47d0064/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d616e616c797369732d726564
[pdf]https://arxiv.org/abs/2010.07100
[code]https://github.com/neulab/REALSumm
https://camo.githubusercontent.com/24cd0b763396c976c32ddb3c711fa1b724f09ff1174f5cd0ef8d9f4868e31cfe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6576616c756174696f6e2d627269676874677265656e
[pdf]https://arxiv.org/abs/2010.05139
[code]https://github.com/zide05/CDEvalSumm
https://camo.githubusercontent.com/24cd0b763396c976c32ddb3c711fa1b724f09ff1174f5cd0ef8d9f4868e31cfe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6576616c756174696f6e2d627269676874677265656e
[pdf]https://arxiv.org/abs/2010.04529
https://camo.githubusercontent.com/87b60393e5b05b455218f8c2f7b89fbb204061853de0b4ffe0e045cab47d0064/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d616e616c797369732d726564
[pdf]https://arxiv.org/abs/2004.13983
https://camo.githubusercontent.com/c77f9f4db06a35c0933ce7c0ac7c137545b324e7495a1aef0812b3738608b3a8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d626961732d6f72616e6765
[pdf]https://arxiv.org/abs/2004.08795
[code]https://github.com/maszhongming/MatchSum
https://camo.githubusercontent.com/3ae519308d8e5033e78534d879a0848e7f2ca20019d6bd01f8072962b71179cb/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6172636869746563747572652d626c7565
https://camo.githubusercontent.com/c77f9f4db06a35c0933ce7c0ac7c137545b324e7495a1aef0812b3738608b3a8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d626961732d6f72616e6765
[pdf]https://www.aclweb.org/anthology/D19-1051/
https://camo.githubusercontent.com/87b60393e5b05b455218f8c2f7b89fbb204061853de0b4ffe0e045cab47d0064/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d616e616c797369732d726564
[pdf]https://arxiv.org/abs/1908.11723
[code]https://github.com/dykang/biassum
https://camo.githubusercontent.com/c77f9f4db06a35c0933ce7c0ac7c137545b324e7495a1aef0812b3738608b3a8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d626961732d6f72616e6765
[pdf]https://arxiv.org/abs/1909.13705
https://camo.githubusercontent.com/c77f9f4db06a35c0933ce7c0ac7c137545b324e7495a1aef0812b3738608b3a8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d626961732d6f72616e6765
[pdf]https://arxiv.org/abs/1909.04028
https://camo.githubusercontent.com/c77f9f4db06a35c0933ce7c0ac7c137545b324e7495a1aef0812b3738608b3a8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d626961732d6f72616e6765
[pdf]https://arxiv.org/abs/1907.03491
[code]https://github.com/maszhongming/Effective_Extractive_Summarization
https://camo.githubusercontent.com/3ae519308d8e5033e78534d879a0848e7f2ca20019d6bd01f8072962b71179cb/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6172636869746563747572652d626c7565
[pdf]https://www.aclweb.org/anthology/D18-1208/
[code]https://github.com/kedz/nnsum/tree/emnlp18-release
https://camo.githubusercontent.com/3ae519308d8e5033e78534d879a0848e7f2ca20019d6bd01f8072962b71179cb/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6172636869746563747572652d626c7565
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#thesis
[pdf]https://tuprints.ulb.tu-darmstadt.de/9012/
[pdf]http://lipiji.com/docs/thesis.pdf
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#theory
[pdf]https://arxiv.org/abs/2110.04480
[pdf]https://arxiv.org/abs/2104.07210
[code]https://github.com/yixinL7/Refactoring-Summarization
[pdf]https://tuprints.ulb.tu-darmstadt.de/9012/
https://camo.githubusercontent.com/5199d3ed889691d2e966bff4a297386114af3e10aa8f7378b2ac84e08f13a10a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d7468657369732d726564
[pdf]https://arxiv.org/abs/2010.06213
[code]https://github.com/epfl-dlab/KLearn
[pdf]https://www.aclweb.org/anthology/P19-1101/
[pdf]https://arxiv.org/abs/1909.07405
[code]https://github.com/peterwestuw/BottleSum
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#dataset
CNN-DailyMailhttps://github.com/harvardnlp/sent-summary
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond https://www.aclweb.org/anthology/K16-1028/
New York Timeshttps://catalog.ldc.upenn.edu/LDC2008T19
The New York Times Annotated Corpushttps://catalog.ldc.upenn.edu/LDC2008T19
DUChttps://duc.nist.gov/data.html
The Effects Of Human Variation In DUC Summarization Evaluationhttps://www.aclweb.org/anthology/W04-1003/
Gigawordhttps://github.com/harvardnlp/sent-summary
A Neural Attention Model For Abstractive Sentence Summarizationhttps://arxiv.org/abs/1509.00685
Newsroomhttp://lil.nlp.cornell.edu/newsroom/
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategieshttps://www.aclweb.org/anthology/N18-1065
Xsumhttps://github.com/EdinburghNLP/XSum
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationhttps://www.aclweb.org/anthology/D18-1206/
Multi-Newshttps://github.com/Alex-Fabbri/Multi-News
Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Modelhttps://arxiv.org/abs/1906.01749
SAMSumhttps://arxiv.org/abs/1911.12237
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationhttps://arxiv.org/abs/1911.12237
AMIhttp://groups.inf.ed.ac.uk/ami/download/
The AMI Meeting Corpus: A pre-announcement. http://groups.inf.ed.ac.uk/ami/download/
ICSIhttp://groups.inf.ed.ac.uk/ami/icsi/download/
The ICSI Meeting Corpushttp://groups.inf.ed.ac.uk/ami/icsi/
MSMOhttp://www.nlpr.ia.ac.cn/cip/jjzhang.htm
MSMO: Multimodal Summarization with Multimodal Outputhttps://www.aclweb.org/anthology/D18-1448/
How2https://github.com/srvk/how2-dataset
How2: A Large-scale Dataset for Multimodal Language Understandinghttps://arxiv.org/abs/1811.00347
ScisummNethttps://cs.stanford.edu/~myasu/projects/scisumm_net/
ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networkshttps://arxiv.org/abs/1909.01716
PubMed, ArXivhttps://github.com/armancohan/long-summarization
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentshttps://arxiv.org/abs/1804.05685
TALKSUMMhttps://github.com/levguy/talksumm
TALKSUMM: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talkshttps://www.aclweb.org/anthology/P19-1204/
BillSumhttps://github.com/FiscalNote/BillSum
BillSum: A Corpus for Automatic Summarization of US Legislationhttps://www.aclweb.org/anthology/D19-5406/
LCSTShttp://icrc.hitsz.edu.cn/Article/show/139.html
https://camo.githubusercontent.com/b03277bfadaa193d7391a74767f590e8a5e66158b8b15a084e425b5cb32db4e3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4368696e6573652d6f72616e6765
LCSTS: A Large Scale Chinese Short Text Summarization Dataset https://www.aclweb.org/anthology/D15-1229/
WikiHowhttps://github.com/mahnazkoupaee/WikiHow-Dataset
WikiHow: A Large Scale Text Summarization Datasethttps://arxiv.org/abs/1810.09305
Concept-map-based MDS Corpushttps://github.com/UKPLab/emnlp2017-cmapsum-corpus/
Bringing Structure into Summaries : Crowdsourcing a Benchmark Corpus of Concept Mapshttps://www.aclweb.org/anthology/D17-1320/
WikiSumhttps://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wikisum
Generating Wikipedia By Summarizing Long Sequencehttps://arxiv.org/abs/1801.10198
GameWikiSumhttps://github.com/Diego999/GameWikiSum
GameWikiSum : a Novel Large Multi-Document Summarization Datasethttps://arxiv.org/abs/2002.06851
En2Zh CLS, Zh2En CLShttp://www.nlpr.ia.ac.cn/cip/dataset.htm
https://camo.githubusercontent.com/b03277bfadaa193d7391a74767f590e8a5e66158b8b15a084e425b5cb32db4e3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4368696e6573652d6f72616e6765
NCLS: Neural Cross-Lingual Summarizationhttps://arxiv.org/abs/1909.00156
Timeline Summarization Datasethttps://github.com/yingtaomj/Learning-towards-Abstractive-Timeline-Summarization
Learning towards Abstractive Timeline Summarization https://www.ijcai.org/Proceedings/2019/686
Reddit TIFUhttps://github.com/ctr4si/MMN
Abstractive Summarization of Reddit Posts with Multi-level Memory Networkshttps://arxiv.org/abs/1811.00783
TripAtthttps://github.com/Junjieli0704/ASN
Attribute-aware Sequence Network for Review Summarizationhttps://www.aclweb.org/anthology/D19-1297/
Reader Comments Summarization Corpushttps://drive.google.com/file/d/1_YH5cBtvNnUNJjGj7kiTMjuHydBqWYQT/view?usp=drive_open
Abstractive Text Summarization by Incorporating Reader Comments https://arxiv.org/abs/1812.05407
BIGPATENThttps://evasharma.github.io/bigpatent/
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarizationhttps://arxiv.org/abs/1906.03741
Curation Corpushttps://github.com/CurationCorp/curation-corpus
Curation Corpus for Abstractive Text Summarisationhttps://github.com/CurationCorp/curation-corpus
MATINFhttps://github.com/WHUIR/MATINF
https://camo.githubusercontent.com/b03277bfadaa193d7391a74767f590e8a5e66158b8b15a084e425b5cb32db4e3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4368696e6573652d6f72616e6765
MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarizationhttps://arxiv.org/abs/2004.12302
MLSUMhttps://github.com/recitalAI/MLSUM
MLSUM: The Multilingual Summarization Corpushttps://arxiv.org/abs/2004.14900
Using Summarization to Discover Argument Facets in Online Idealogical Dialoghttps://www.aclweb.org/anthology/N15-1046/
WCEPhttps://github.com/complementizer/wcep-mds-dataset
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portalhttps://arxiv.org/abs/2005.10070
ArgKPhttps://www.research.ibm.com/haifa/dept/vst/debating_data.shtml
From Arguments to Key Points: Towards Automatic Argument Summarizationhttps://arxiv.org/abs/2005.01619
CRD3https://github.com/RevanthRameshkumar/CRD3
Storytelling with Dialogue: A Critical Role Dungeons and Dragons Datasethttps://www.aclweb.org/anthology/2020.acl-main.459/
Gazetahttps://github.com/IlyaGusev/gazeta
Dataset for Automatic Summarization of Russian Newshttps://arxiv.org/abs/2006.11063
MINDhttps://msnews.github.io/
MIND: A Large-scale Dataset for News Recommendationhttps://www.aclweb.org/anthology/2020.acl-main.331/
public_meetingshttps://github.com/pltrdy/autoalign
Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creationhttps://www.aclweb.org/anthology/2020.lrec-1.829
Building a Dataset for Summarization and Keyword Extraction from Emailshttps://www.aclweb.org/anthology/L14-1028/
BC3https://www.cs.ubc.ca/cs-research/lci/research-groups/natural-language-processing/bc3.html
A publicly available annotated corpus for supervised email summarizationhttps://www.ufv.ca/media/assets/computer-information-systems/gabriel-murray/publications/aaai08.pdf
WikiLinguahttps://github.com/esdurmus/Wikilingua
https://camo.githubusercontent.com/b03277bfadaa193d7391a74767f590e8a5e66158b8b15a084e425b5cb32db4e3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4368696e6573652d6f72616e6765
WikiLingua- A New Benchmark Dataset for Cross-Lingual Abstractive Summarizationhttps://arxiv.org/abs/2010.03093
LcsPIRThttp://eie.usts.edu.cn/prj/NLPoSUST/LcsPIRT.htm
https://camo.githubusercontent.com/b03277bfadaa193d7391a74767f590e8a5e66158b8b15a084e425b5cb32db4e3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4368696e6573652d6f72616e6765
Global Encoding for Long Chinese Text Summarizationhttps://dl.acm.org/doi/10.1145/3407911
CLTShttps://github.com/lxj5957/CLTS-Dataset
CLTS-plushttps://github.com/lxj5957/CLTS-plus-Dataset
https://camo.githubusercontent.com/b03277bfadaa193d7391a74767f590e8a5e66158b8b15a084e425b5cb32db4e3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4368696e6573652d6f72616e6765
CLTS: A New Chinese Long Text Summarization Datasethttps://link.springer.com/chapter/10.1007/978-3-030-60450-9_42
CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summarieshttps://arxiv.org/abs/2206.04253
VMSMOhttps://github.com/yingtaomj/VMSMO
VMSMO: Learning to Generate Multimodal Summary for Video-based News Articleshttps://arxiv.org/abs/2010.05406
Multi-XSciencehttps://github.com/yaolu/Multi-XScience
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articleshttps://arxiv.org/abs/2010.14235
SCITLDRhttps://github.com/allenai/scitldr
TLDR: Extreme Summarization of Scientific Documentshttps://arxiv.org/abs/2004.15011
scisumm-corpushttps://github.com/WING-NUS/scisumm-corpus
QBSUMhttps://www.dropbox.com/sh/t2cp7ml1kb8ako0/AADmS2RMfJvLbukyQbb08CGGa?dl=0
https://camo.githubusercontent.com/b03277bfadaa193d7391a74767f590e8a5e66158b8b15a084e425b5cb32db4e3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4368696e6573652d6f72616e6765
QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applicationshttps://arxiv.org/abs/2010.14108
qMDShttps://github.com/google-research-datasets/aquamuse
AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarizationhttps://arxiv.org/abs/2010.12694
Liputan6https://github.com/fajri91/sum_liputan6
Liputan6: A Large-scale Indonesian Dataset for Text Summarizationhttps://arxiv.org/pdf/2011.00679.pdf
SportsSumhttps://github.com/ej0cl6/SportsSum
https://camo.githubusercontent.com/b03277bfadaa193d7391a74767f590e8a5e66158b8b15a084e425b5cb32db4e3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4368696e6573652d6f72616e6765
Generating Sports News from Live Commentary: A Chinese Dataset for Sports Game Summarizationhttps://khhuang.me/docs/aacl2020sportssum.pdf
WikiAsphttps://github.com/neulab/wikiasp
WikiAsp: A Dataset for Multi-domain Aspect-based Summarizationhttps://arxiv.org/abs/2011.07832
DebateSumhttps://github.com/Hellisotherpeople/DebateSum
https://camo.githubusercontent.com/4ab8b96f8390282b76d19bdc52fa3b0b9331ef3722eea5b7de251d0a06ace40f/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d5175657279253230466f63757365642d707572706c65
DebateSum:A large-scale argument mining and summarization datasethttps://arxiv.org/abs/2011.07251
Open4Businesshttps://github.com/amanpreet692/Open4Business
Open4Business (O4B): An Open Access Dataset for Summarizing Business Documentshttps://arxiv.org/abs/2011.07636
OrangeSumhttps://github.com/moussaKam/OrangeSum
BARThez: a Skilled Pretrained French Sequence-to-Sequence Modelhttps://arxiv.org/abs/2010.12321
Medical Conversationhttps://github.com/cuhksz-nlp/HET-MC
https://camo.githubusercontent.com/b03277bfadaa193d7391a74767f590e8a5e66158b8b15a084e425b5cb32db4e3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4368696e6573652d6f72616e6765
Summarizing Medical Conversations via Identifying Important Utteranceshttps://www.aclweb.org/anthology/2020.coling-main.63/
SumTitleshttps://github.com/huawei-noah/sumtitles
SumTitles: a Summarization Dataset with Low Extractivenesshttps://www.aclweb.org/anthology/2020.coling-main.503/
BANShttps://www.kaggle.com/datasets/prithwirajsust/bengali-news-summarization-dataset
Bengali Abstractive News Summarization (BANS): A Neural Attention Approachhttps://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers/blob/main
e-commercehttps://github.com/ypnlp/coling
https://camo.githubusercontent.com/b03277bfadaa193d7391a74767f590e8a5e66158b8b15a084e425b5cb32db4e3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4368696e6573652d6f72616e6765
On the Faithfulness for E-commerce Product Summarizationhttps://www.aclweb.org/anthology/2020.coling-main.502/
TWEETSUMhttps://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers/blob/main
TWEETSUM: Event-oriented Social Summarization Datasethttps://www.aclweb.org/anthology/2020.coling-main.504/
SPACEhttps://github.com/stangelid/qt
Extractive Opinion Summarization in Quantized Transformer Spaceshttps://arxiv.org/abs/2012.04443
pn-summaryhttps://github.com/hooshvare/pn-summary
Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarizationhttps://arxiv.org/abs/2012.11204
E-commerce1https://github.com/RowitZou/topic-dialog-summ
Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modelinghttps://arxiv.org/abs/2012.07311
E-commerce2https://github.com/RowitZou/RankAE
Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encodershttps://arxiv.org/abs/2012.07300
BengaliSummarizationhttps://github.com/tafseer-nayeem/BengaliSummarization
Unsupervised Abstractive Summarization of Bengali Text Documentshttps://arxiv.org/abs/2102.04490
MediaSumhttps://github.com/zcgzcgzcg1/MediaSum
MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarizationhttps://arxiv.org/abs/2103.06410
Healthline and BreastCancerhttps://github.com/darsh10/Nutribullets
Nutri-bullets: Summarizing Health Studies by Composing Segmentshttps://arxiv.org/abs/2103.11921
GOVREPORThttps://gov-report-data.github.io/
Efficient Attentions for Long Document Summarizationhttps://arxiv.org/abs/2104.02112
SSNhttps://github.com/ChenxinAn-fdu/CGSum
Enhancing Scientific Papers Summarization with Citation Graphhttps://arxiv.org/abs/2104.03057
MTSampleshttps://github.com/babylonhealth/medical-note-summarisation
Towards objectively evaluating the quality of generated medical summarieshttps://arxiv.org/abs/2104.04412
QMSumhttps://github.com/Yale-LILY/QMSum
QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarizationhttps://arxiv.org/abs/2104.05938
MS2https://github.com/allenai/ms2
MS2: Multi-Document Summarization of Medical Studieshttps://arxiv.org/abs/2104.06486
SummScreenhttps://github.com/mingdachen/SummScreen
SummScreen: A Dataset for Abstractive Screenplay Summarizationhttps://aclanthology.org/2022.acl-long.589/
SciDuethttps://github.com/IBM/document2slides
D2S: Document-to-Slide Generation Via Query-Based Text Summarizationhttps://github.com/IBM/document2slides
MultiHumEShttps://deephelp.zendesk.com/hc/en-us/sections/360011925552-MultiHumES
MultiHumES: Multilingual Humanitarian Dataset for Extractive Summarizationhttps://www.aclweb.org/anthology/2021.eacl-main.146/
DialSummhttps://github.com/cylnlp/DialSumm
DialSumm: A Real-Life Scenario Dialogue Summarization Datasethttps://arxiv.org/abs/2105.06762
BookSumhttps://github.com/salesforce/booksum
BookSum: A Collection of Datasets for Long-form Narrative Summarizationhttps://arxiv.org/abs/2105.08209
CLEShttp://icrc.hitsz.edu.cn/xszy/yjzy.htm
https://camo.githubusercontent.com/b03277bfadaa193d7391a74767f590e8a5e66158b8b15a084e425b5cb32db4e3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4368696e6573652d6f72616e6765
A Large-Scale Chinese Long-Text Extractive Summarization Corpushttps://ieeexplore.ieee.org/abstract/document/9414946
FacetSumhttps://github.com/hfthair/emerald_crawler
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documentshttps://aclanthology.org/2021.acl-short.137/
ConvoSummhttps://github.com/Yale-LILY/ConvoSumm
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mininghttps://aclanthology.org/2021.acl-long.535/
AgreeSumhttps://github.com/google-research-datasets/AgreeSum
AgreeSum: Agreement-Oriented Multi-Document Summarizationhttps://arxiv.org/abs/2106.02278
En2Dehttps://github.com/ybai-nlp/MCLAS
Cross-Lingual Abstractive Summarization with Limited Parallel Resourceshttps://arxiv.org/abs/2105.13648
VT-SSumhttps://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers/blob/main
VT-SSum: A Benchmark Dataset for Video Transcript Segmentation and Summarizationhttps://arxiv.org/abs/2106.05606
AESLChttps://github.com/ryanzhumich/AESLC
This Email Could Save Your Life: Introducing the Task of Email Subject Line Generationhttps://www.aclweb.org/anthology/P19-1043/
XL-Sumhttps://github.com/csebuetnlp/xl-sum
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languageshttp://rifatshahriyar.github.io/files/XL-Sum.pdf
TES 2012-2016https://github.com/JoeBloggsIR/TSSuBERT
TSSuBERT: Tweet Stream Summarization Using BERThttps://arxiv.org/abs/2106.08770
PENShttps://msnews.github.io/pens.html
PENS: A Dataset and Generic Framework for Personalized News Headline Generationhttps://www.microsoft.com/en-us/research/uploads/prod/2021/06/ACL2021_PENS_Camera_Ready_1862_Paper.pdf
XSum Hallucination Annotationshttps://github.com/google-research-datasets/xsum_hallucination_annotations
On Faithfulness and Factuality in Abstractive Summarizationhttps://arxiv.org/abs/2005.00661
factuality-datasetshttps://github.com/tagoyal/factuality-datasets#factuality-datasets
Annotating and Modeling Fine-grained Factuality in Summarizationhttps://arxiv.org/abs/2104.04302
frankhttps://github.com/artidoro/frank
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricshttps://arxiv.org/abs/2104.13346
TRIPODhttps://github.com/ppapalampidi/GraphTP
Movie Summarization via Sparse Graph Constructionhttps://arxiv.org/abs/2012.07536
AdaptSumhttps://github.com/TysonYu/AdaptSum
AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarizationhttps://arxiv.org/abs/2103.11332
PTShttps://github.com/FeiSun/ProductTitleSummarizationCorpus
Multi-Source Pointer Network for Product Title Summarizationhttps://arxiv.org/abs/1808.06885
RAMDShttps://github.com/lipiji/vae-salience-ramds
Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Datasethttps://arxiv.org/abs/1708.01065
court judgmenthttps://github.com/gsh199449/proto-summ
How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editinghttps://arxiv.org/abs/1909.08837
ADEGBTShttps://github.com/MMLabTHUSZ/ADEGBTS
A Dataset for Exploring Gaze Behaviors in Text Summarizationhttps://dl.acm.org/doi/abs/10.1145/3339825.3394928
MeQSumhttps://github.com/abachaa/MeQSum
On the Summarization of Consumer Health Questionshttps://www.aclweb.org/anthology/P19-1215/
OpoSumhttps://github.com/stangelid/oposum
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervisedhttps://www.aclweb.org/anthology/D18-1403/
MM-AVShttps://github.com/xiyan524/MM-AVS
Multi-modal Summarization for Video-containing Documentshttps://arxiv.org/abs/2009.08018
WikiCatSumhttps://github.com/lauhaide/WikiCatSum
Generating Summaries with Topic Templates and Structured Convolutional Decodershttps://arxiv.org/abs/1906.04687
SDF-TLShttps://github.com/MorenoLaQuatra/SDF-TLS
Summarize Dates First: A Paradigm Shift in Timeline Summarizationhttps://dl.acm.org/doi/10.1145/3404835.3462954
RWS-Cithttps://github.com/jingqiangchen/RWS-Cit
*Automatic generation of related work through summarizing citationshttps://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.4261
MTLShttps://yiyualt.github.io/mtlsdata/
Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summarieshttps://aclanthology.org/2021.acl-long.32/
EMAILSUMhttps://github.com/ZhangShiyue/EmailSum
EmailSum: Abstractive Email Thread Summarizationhttps://aclanthology.org/2021.acl-long.537/
WikiSumhttps://registry.opendata.aws/wikisum/
WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluationhttps://aclanthology.org/2021.acl-short.28/
SumPubMedhttps://github.com/vgupta123/sumpubmed
SumPubMed: Summarization Dataset of PubMed Scientific Articleshttps://aclanthology.org/2021.acl-srw.30/
MLGSumhttps://github.com/brxx122/CALMS
Contrastive Aligned Joint Learning for Multilingual Summarizationhttps://aclanthology.org/2021.findings-acl.242/
SMARTPHONE,COMPUTERhttps://github.com/JD-AI-Research-NLP/CUSTOM
CUSTOM: Aspect-Oriented Product Summarization for E-Commercehttps://arxiv.org/abs/2108.08010
CSDShttps://github.com/xiaolinAndy/CSDS
CSDS: A Fine-grained Chinese Dataset for Customer Service Dialogue Summarizationhttps://arxiv.org/abs/2108.13139
persian-datasethttps://github.com/mohammadiahmad/persian-dataset
ARMAN: Pre-training with Semantically Selecting and Reordering of Sentences for Persian Abstractive Summarizationhttps://arxiv.org/abs/2109.04098
StreamHoverhttps://github.com/ucfnlp/streamhover
StreamHover: Livestream Transcript Summarization and Annotationhttps://arxiv.org/abs/2109.05160
CNewSumhttps://dqwang122.github.io/projects/CNewSum/
https://camo.githubusercontent.com/b03277bfadaa193d7391a74767f590e8a5e66158b8b15a084e425b5cb32db4e3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4368696e6573652d6f72616e6765
CNewSum: A Large-scale Chinese News Summarization Dataset with Human-annotated Adequacy and Deducibility Levelhttps://lileicc.github.io/pubs/wang2021cnewsum.pdf
MiRANewshttps://github.com/XinnuoXu/MiRANews
MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarizationhttps://arxiv.org/abs/2109.10650
HowSummhttps://github.com/odelliab/HowSumm
HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articleshttps://arxiv.org/abs/2110.03179
SportsSum2.0https://github.com/krystalan/SportsSum2.0
SportsSum2.0: Generating High-Quality Sports News from Live Text Commentaryhttps://arxiv.org/abs/2110.05750
CoCoSumhttps://github.com/megagonlabs/cocosum
Comparative Opinion Summarization via Collaborative Decodinghttps://arxiv.org/abs/2110.07520
MReDhttps://github.com/Shen-Chenhui/MReD/
MReD: A Meta-Review Dataset for Controllable Text Generationhttps://arxiv.org/abs/2110.07474
MSˆ2https://github.com/allenai/ms2
MSˆ2: Multi-Document Summarization of Medical Studieshttps://aclanthology.org/2021.emnlp-main.594/
MassiveSummhttps://github.com/danielvarab/massive-summ
MassiveSumm: a very large-scale, very multilingual, news summarisation datasethttps://aclanthology.org/2021.emnlp-main.797/
XWikishttps://github.com/lauhaide/clads
Models and Datasets for Cross-Lingual Summarisationhttps://aclanthology.org/2021.emnlp-main.742/
SUBSUMEhttps://github.com/afariha/SubSumE
SUBSUME: A Dataset for Subjective Summary Extraction from Wikipedia Documentshttps://aclanthology.org/2021.newsum-1.14/
TLDR9+https://github.com/sajastu/reddit_collector
TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Postshttps://aclanthology.org/2021.newsum-1.15/
20 Minutenhttps://github.com/ZurichNLP/20Minuten
A New Dataset and Efficient Baselines for Document-level Text Simplification in Germanhttps://aclanthology.org/2021.newsum-1.16/
WSDhttps://github.com/MehwishFatimah/wsd
A Novel Wikipedia based Dataset for Monolingual and Cross-Lingual Summarizationhttps://aclanthology.org/2021.newsum-1.5/
TEDSummaryhttps://github.com/nttcslab-sp-admin/TEDSummary
Attention-based Multi-hypothesis Fusion for Speech Summarizationhttps://arxiv.org/abs/2111.08201
SummaC Benchmarkhttps://github.com/tingofurro/summac/
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationhttps://arxiv.org/abs/2111.09525
ForumSumhttps://huggingface.co/datasets/forumsum
K-SportsSumhttps://github.com/krystalan/K-SportsSum
Knowledge Enhanced Sports Game Summarizationhttps://arxiv.org/abs/2111.12535
Test-Amazonhttps://github.com/abrazinskas/Copycat-abstractive-opinion-summarizer
Unsupervised Opinion Summarization as Copycat-Review Generationhttps://aclanthology.org/2020.acl-main.461/
Test-Amazon-Yelphttps://github.com/abrazinskas/FewSum
Few-Shot Learning for Opinion Summarizationhttps://aclanthology.org/2020.emnlp-main.337/
AmaSumhttps://github.com/abrazinskas/SelSum
Learning Opinion Summarizers by Selecting Informative Reviewshttps://aclanthology.org/2021.emnlp-main.743/
CrossSumhttps://github.com/csebuetnlp/CrossSum
CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summarization for 1500+ Language Pairshttps://arxiv.org/abs/2112.08804
HCSCL-MSDatasethttps://github.com/LitianD/HCSCL-MSDataset
Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarizationhttps://arxiv.org/abs/2112.12072
Klexikonhttps://github.com/dennlinger/klexikon
Klexikon: A German Dataset for Joint Summarization and Simplificationhttps://arxiv.org/abs/2201.07198
TODSumhttps://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers/blob/main
TODSum: Task-Oriented Dialogue Summarization with State Trackinghttps://arxiv.org/abs/2110.12680
TWEETSUMMhttps://aclanthology.org/2021.findings-emnlp.24/
TWEETSUMM - A Dialog Summarization Dataset for Customer Servicehttps://aclanthology.org/2021.findings-emnlp.24/
PeerSumhttps://github.com/oaimli/PeerSum
PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarizationhttps://arxiv.org/abs/2203.01769
Celebrity TS, Event TS, Wiki TShttps://github.com/iriscxy/Unified-Timeline-Summarizer
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Orderhttps://dl.acm.org/doi/abs/10.1145/3517221
Chart-to-Texthttps://github.com/vis-nlp/Chart-to-text
Chart-to-Text: A Large-Scale Benchmark for Chart Summarizationhttps://arxiv.org/abs/2203.06486
GovReport-QShttps://gov-report-data.github.io/
HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarizationhttps://arxiv.org/abs/2203.10741
EntSUMhttps://zenodo.org/record/6359875
EntSUM: A Data Set for Entity-Centric Summarizationhttps://github.com/bloomberg/entsum
ALLSIDEShttps://github.com/HLTCHKUST/framing-bias-metric
NeuS: Neutral Multi-News Summarization for Mitigating Framing Biashttps://arxiv.org/abs/2204.04902
GRAPHELSUMShttps://github.com/maartjeth/summarization_with_graphical_elements
Summarization with Graphical Elementshttps://arxiv.org/abs/2204.07551
Annotated-Wikilarge-Newselahttps://github.com/AshOlogn/Evaluating-Factuality-in-Text-Simplification
Evaluating Factuality in Text Simplificationhttps://arxiv.org/abs/2204.07562
WikiMultihttps://github.com/tikhonovpavel/wikimulti
WikiMulti: a Corpus for Cross-Lingual Summarizationhttps://arxiv.org/abs/2204.11104
Welshhttps://github.com/UCREL/welsh-summarization-dataset
Introducing the Welsh Text Summarisation Dataset and Baseline Systemshttps://arxiv.org/abs/2205.02545
SuMehttps://stonybrooknlp.github.io/SuMe/
SuMe: A Dataset Towards Summarizing Biomedical Mechanismshttps://arxiv.org/abs/2205.04652
CiteSumhttps://github.com/morningmoni/CiteSum
CiteSum: Citation Text-guided Scientific Extreme Summarization and Low-resource Domain Adaptationhttps://arxiv.org/abs/2205.06207
MSAMSumhttps://github.com/xcfcode/MSAMSum
MSAMSum: Towards Benchmarking Multi-lingual Dialogue Summarizationhttps://aclanthology.org/2022.dialdoc-1.1/
SQuALITYhttps://github.com/nyu-mll/SQuALITY
SQuALITY: Building a Long-Document Summarization Dataset the Hard Wayhttps://aclanthology.org/2022.emnlp-main.75/
X-SCITLDRhttps://github.com/sobamchan/xscitldr
X-SCITLDR: Cross-Lingual Extreme Summarization of Scholarly Documentshttps://arxiv.org/abs/2205.15051
NEWTShttps://github.com/ali-bahrainian/NEWTS
NEWTS: A Corpus for News Topic-Focused Summarizationhttps://arxiv.org/abs/2205.15661
EntSUMhttps://github.com/bloomberg/entsum
EntSUM: A Data Set for Entity-Centric Extractive Summarizationhttps://aclanthology.org/2022.acl-long.237/
ASPECTNEWShttps://github.com/oja/aosumm
ASPECTNEWS: Aspect-Oriented Summarization of News Documentshttps://aclanthology.org/2022.acl-long.449/
RNSumhttps://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers/blob/main
RNSum: A Large-Scale Dataset for Automatic Release Note Generation via Commit Logs Summarizationhttps://aclanthology.org/2022.acl-long.597/
AnswerSummhttps://github.com/Alex-Fabbri/AnswerSumm
AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarizationhttps://arxiv.org/abs/2111.06474
CHQ-Summhttps://github.com/shwetanlp/Yahoo-CHQ-Summ
CHQ-Summ: A Dataset for Consumer Healthcare Question Summarizationhttps://arxiv.org/abs/2206.06581
Multi-LexSumhttps://github.com/multilexsum/dataset
Real-World Summaries of Civil Rights Lawsuits at Multiple Granularitieshttps://arxiv.org/abs/2206.10883
DACSAhttps://xarrador.dsic.upv.es/resources/dacsa
DACSA: A large-scale Dataset for Automatic summarization of Catalan and Spanish newspaper Articleshttps://aclanthology.org/2022.naacl-main.434/
BigSurveyhttps://github.com/StevenLau6/BigSurvey
Generating a Structured Summary of Numerous Academic Papers: Dataset and Methodhttps://www.ijcai.org/proceedings/2022/0591.pdf
CSLhttps://github.com/ydli-ai/CSL
https://camo.githubusercontent.com/b03277bfadaa193d7391a74767f590e8a5e66158b8b15a084e425b5cb32db4e3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d4368696e6573652d6f72616e6765
CSL: A Large-scale Chinese Scientific Literature Datasethttps://arxiv.org/abs/2209.05034
PCC Summarieshttps://github.com/fhewett/pcc-summaries
Extractive Summarisation for German-language Data: A Text-level Approach with Discourse Featureshttps://aclanthology.org/2022.coling-1.63/
LipKeyhttps://github.com/fajri91/LipKey
LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarizationhttps://aclanthology.org/2022.coling-1.303/
PLOShttps://github.com/TGoldsack1/Corpora_for_Lay_Summarisation
Making Science Simple: Corpora for the Lay Summarisation of Scientific Literaturehttps://arxiv.org/abs/2210.09932
eLifehttps://github.com/TGoldsack1/Corpora_for_Lay_Summarisation
Making Science Simple: Corpora for the Lay Summarisation of Scientific Literaturehttps://arxiv.org/abs/2210.09932
ECTSumhttps://github.com/rajdeep345/ECTSum
ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcriptshttps://arxiv.org/abs/2210.12467
EUR-Lex-Sumhttps://github.com/achouhan93/eur-lex-sum
EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domainhttps://arxiv.org/abs/2210.13448
CrisisLTLSumhttps://github.com/CrisisLTLSum/CrisisTimelines
CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarizationhttps://arxiv.org/abs/2210.14190
LANS: Large-scale Arabic News Summarization Corpushttps://arxiv.org/abs/2210.13600
MACSUMhttps://github.com/psunlpgroup/MACSum
MACSUM: Controllable Summarization with Mixed Attributeshttps://arxiv.org/abs/2211.05041
NarraSumhttps://github.com/zhaochaocs/narrasum
NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarizationhttps://arxiv.org/abs/2212.01476
LoRaLayhttps://github.com/recitalAI/loralay-datasets
LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarizationhttps://arxiv.org/abs/2301.11312
HunSum-1https://github.com/dorinapetra/summarization
HunSum-1: an Abstractive Summarization Dataset for Hungarianhttps://arxiv.org/abs/2302.00455
MCLShttps://github.com/korokes/MCLS
Assist Non-native Viewers: Multimodal Cross-Lingual Summarization for How2 Videoshttps://aclanthology.org/2022.emnlp-main.468/
JDDC 2.1https://github.com/hrlinlp/jddc2.1
JDDC 2.1: A Multimodal Chinese Dialogue Dataset with Joint Tasks of Query Rewriting, Response Generation, Discourse Parsing, and Summarizationhttps://aclanthology.org/2022.emnlp-main.825/
CroCoSumhttps://github.com/RosenZhang/CroCoSum
CroCoSum: A Benchmark Dataset for Cross-Lingual Code-Switched Summarizationhttps://arxiv.org/abs/2303.04092
unarXivehttps://github.com/IllDepence/unarXive
unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadatahttps://link.springer.com/article/10.1007/s11192-020-03382-z
TempoSumhttps://github.com/AndyCheang/TempoSum
TempoSum: Evaluating the Temporal Generalization of Abstractive Summarizationhttps://arxiv.org/abs/2305.01951
VCSUMhttps://github.com/hahahawu/VCSum
VCSUM: A Versatile Chinese Meeting Summarization Datasethttps://arxiv.org/abs/2305.05280
MeetingBankhttps://meetingbank.github.io/
MeetingBank: A Benchmark Dataset for Meeting Summarizationhttps://aclanthology.org/2023.acl-long.906/
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#dialogue
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#dataset-1
[pdf]https://aclanthology.org/2023.acl-long.906/
[data]https://meetingbank.github.io/
[pdf]https://arxiv.org/abs/2210.12467
[data]https://github.com/rajdeep345/ECTSum
[pdf]https://arxiv.org/abs/2110.12680
[pdf]https://aclanthology.org/2021.findings-emnlp.24/
[data]https://github.com/guyfe/Tweetsumm
[pdf]https://aclanthology.org/2021.findings-emnlp.391/
[data]https://huggingface.co/datasets/forumsum
[pdf]https://aclanthology.org/2021.emnlp-main.365/
[data]https://github.com/xiaolinAndy/CSDS
[pdf]https://aclanthology.org/2021.acl-long.537/
[data]https://github.com/ZhangShiyue/EmailSum
[pdf]https://arxiv.org/abs/2105.06762
[data]https://github.com/cylnlp/DialSumm
[pdf]https://aclanthology.org/2021.acl-long.535/
[code]https://github.com/Yale-LILY/ConvoSumm
[pdf]https://arxiv.org/abs/2103.06410
[code]https://github.com/zcgzcgzcg1/MediaSum
[pdf]https://arxiv.org/abs/2104.05938
[data]https://github.com/Yale-LILY/QMSum
[pdf]https://www.aclweb.org/anthology/2020.acl-main.459/
[data]https://github.com/RevanthRameshkumar/CRD3
[pdf]https://www.aclweb.org/anthology/2020.coling-main.503/
[code]https://github.com/huawei-noah/sumtitles
[pdf]https://www.aclweb.org/anthology/2020.coling-main.63/
[code]https://github.com/cuhksz-nlp/HET-MC
[pdf]https://aclanthology.org/2021.emnlp-main.499/
[code]https://github.com/midas-research/gupshup
[pdf]https://aclanthology.org/2022.acl-long.589/
[data]https://github.com/mingdachen/SummScreen
[pdf]https://arxiv.org/abs/1911.12237
[data]https://arxiv.org/src/1911.12237v2/anc/corpus.7z
[pdf]https://arxiv.org/abs/1811.00185
[pdf]https://link.springer.com/chapter/10.1007/11677482_3
[pdf]https://www.researchgate.net/publication/4015071_The_ICSI_meeting_corpus
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#email-summarization
[pdf]https://faculty.cc.gatech.edu/~dyang888/docs/acl22_summarization.pdf
[pdf]https://aclanthology.org/2021.acl-long.537/
[data]https://github.com/ZhangShiyue/EmailSum
[pdf]https://www.aclweb.org/anthology/2020.acl-main.767/
[code]https://github.com/MSR-LIT/SmartToDo
[bib]https://www.aclweb.org/anthology/2020.acl-main.767.bib
[pdf]https://www.aclweb.org/anthology/2020.aacl-main.32/
[pdf]https://www.aclweb.org/anthology/P19-1043/
[data]https://github.com/ryanzhumich/AESLC
[bib]https://www.aclweb.org/anthology/P19-1043.bib
[pdf]https://www.aclweb.org/anthology/L14-1028/
[pdf]https://www.aaai.org/Papers/Workshops/2008/WS-08-04/WS08-04-014.pdf
[pdf]https://www2007.org/papers/paper631.pdf
[pdf]https://www.aclweb.org/anthology/W04-1008.pdf
[pdf]https://www.aclweb.org/anthology/N04-4027/
[bib]https://www.aclweb.org/anthology/N04-4027.bib
[pdf]https://www.academia.edu/21603342/Facilitating_email_thread_access_by_extractive_summary_generation
[pdf]http://john.blitzer.com/papers/iui.pdf
[pdf]https://www.aclweb.org/anthology/W01-0719/
[bib]https://www.aclweb.org/anthology/W01-0719.bib
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#meeting-summarization
[pdf]https://aclanthology.org/2023.findings-acl.538/
[pdf]https://aclanthology.org/2023.acl-long.731/
[code]https://github.com/hkim-etri/ExplainMeetSum
[pdf]https://aclanthology.org/2023.findings-acl.377/
[data]https://github.com/hahahawu/VCSum
[pdf]https://arxiv.org/abs/2303.04487
[pdf]https://arxiv.org/abs/2210.11374
[demo]https://www.youtube.com/watch?v=TG1pJJo0Iqo&feature=youtu.be
[pdf]https://arxiv.org/abs/2209.06913
[pdf]https://arxiv.org/abs/2208.04163
[pdf]https://arxiv.org/abs/2205.05433
[data]https://github.com/ELITR/alignmeet
[pdf]https://aclanthology.org/2022.findings-naacl.198/
[pdf]https://aclanthology.org/2022.acl-long.112/
[code]https://github.com/psunlpgroup/Summ-N
https://github.com/psunlpgroup/Summ-Nhttps://github.com/psunlpgroup/Summ-N
[pdf]https://arxiv.org/abs/2112.07637
[code]https://github.com/salesforce/query-focused-sum
[pdf]https://aclanthology.org/2021.findings-emnlp.97/
[pdf]https://arxiv.org/abs/2111.08210
[code]https://github.com/wxj77/MeetingSummarization
[pdf]https://aclanthology.org/2021.newsum-1.11/
[pdf]https://arxiv.org/abs/2109.07943
[pdf]https://arxiv.org/abs/2109.04609
[pdf]https://arxiv.org/abs/2109.02492
[code]https://github.com/microsoft/DialogLM
[pdf]https://arxiv.org/abs/2108.13629
[pdf]https://arxiv.org/abs/2108.06310
[pdf]https://sigdial.org/sites/default/files/workshops/conference22/Proceedings/pdf/2021.sigdial-1.56.pdf
[pdf]http://www.interspeech2020.org/uploadfile/pdf/Thu-2-6-2.pdf
[code]https://github.com/potsawee/spoken_summ_div
[pdf]https://www.aclweb.org/anthology/W08-0112/
[pdf]https://www.isca-speech.org/archive/archive_papers/interspeech_2010/i10_2518.pdf
[pdf]https://www.emerald.com/insight/content/doi/10.1108/DTA-09-2017-0062/full/html
[pdf]https://ieeexplore.ieee.org/document/4777863
[pdf]https://ieeexplore.ieee.org/document/4960697
[pdf]https://ieeexplore.ieee.org/document/4777864
[pdf]https://arxiv.org/abs/1609.07035
[pdf]https://www.aclweb.org/anthology/W17-4506/
[bib]https://www.aclweb.org/anthology/W17-4506.bib
[pdf]https://arxiv.org/abs/1805.05271
[code]https://bitbucket.org/dascim/acl2018_abssumm/src
[pdf]https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11605/1160504/Abstractive-meeting-summarization-based-on-an-attentional-neural-model/10.1117/12.2587172.full
[pdf]https://link.springer.com/chapter/10.1007/978-3-030-50316-1_33
[pdf]https://link.springer.com/chapter/10.1007/978-3-030-20521-8_53
[pdf]https://arxiv.org/abs/1609.07033
[pdf]https://www.aclweb.org/anthology/2020.lrec-1.829
[bib]https://www.aclweb.org/anthology/2020.lrec-1.829.bib
[pdf]https://arxiv.org/abs/2012.03502
[code]https://github.com/xcfcode/DDAMS
[pdf]https://arxiv.org/abs/2011.00692
[code]https://github.com/ucfnlp/meeting-domain-terminology
[pdf]https://link.springer.com/content/pdf/10.1007/978-3-030-55393-7_22.pdf
[code]https://github.com/d1jiasheng/DialogueSum
[pdf]https://arxiv.org/abs/1809.05715
[code]https://github.com/MiuLab/DialSum
[pdf]https://arxiv.org/abs/2104.12324
[pdf]https://arxiv.org/abs/2104.07545
[code]https://github.com/birch-research/hierarchical-learning
[pdf]https://arxiv.org/abs/2004.02016
[code]https://github.com/microsoft/HMNet
[unofficial-code]https://github.com/JudeLee19/HMNet-End-to-End-Abstractive-Summarization-for-Meetings
[pdf]https://dl.acm.org/doi/10.1145/3308558.3313619
[pdf]https://arxiv.org/abs/1902.01615
[pdf]https://www.aclweb.org/anthology/P19-1210/
[pdf]https://link.springer.com/article/10.1007/s12046-011-0051-3
[pdf]https://dl.acm.org/doi/abs/10.1145/3379336.3381491
[pdf]https://www.mdpi.com/2414-4088/3/3/50
[pdf]https://dl.acm.org/doi/10.1145/3279981.3279987
[pdf]https://dl.acm.org/doi/10.1145/2993148.2993160
[pdf]https://www.cstr.ed.ac.uk/downloads/publications/2005/murray-eurospeech05.pdf
[pdf]https://ieeexplore.ieee.org/document/1221239
[pdf]https://www.aclweb.org/anthology/2021.adaptnlp-1.24/
[pdf]https://arxiv.org/abs/2007.15296
[pdf]https://www.aclweb.org/anthology/W12-1642.pdf
[pdf]https://arxiv.org/abs/2104.05938
[data]https://github.com/Yale-LILY/QMSum
[pdf]https://www.aclweb.org/anthology/P13-1137.pdf
[pdf]https://arxiv.org/abs/1606.07965
[pdf]https://www.aclweb.org/anthology/W09-3934/
[bib]https://www.aclweb.org/anthology/W09-3934.bib
[pdf]https://arxiv.org/abs/2106.00829
[code]https://github.com/Yale-LILY/ConvoSumm
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#chat-summarization
[pdf]https://aclanthology.org/2023.acl-long.775/
[code]https://github.com/Hannibal046/SDDS
[pdf]https://aclanthology.org/2022.coling-1.548/
[pdf]https://aclanthology.org/2021.findings-emnlp.117/
[code]https://github.com/apexmeister/FINDS
[pdf]https://aclanthology.org/2021.newsum-1.8/
[pdf]https://arxiv.org/abs/2109.14199
[pdf]https://arxiv.org/abs/2109.13070
[pdf]https://aclanthology.org/2021.emnlp-main.499/
[code]https://github.com/midas-research/gupshup
[pdf]https://arxiv.org/abs/2109.04994
[code]https://github.com/Junpliu/ConDigSum
[pdf]https://www.researchgate.net/publication/354162497_Give_the_Truth_Incorporate_Semantic_Slot_into_Abstractive_Dialogue_Summarization
[pdf]https://arxiv.org/abs/2109.04080
[code]https://github.com/RowitZou/DAMS
[pdf]https://www.isca-speech.org/archive/interspeech_2021/lee21_interspeech.html
[pdf]https://www.sciencedirect.com/science/article/pii/S0950705121005906
[pdf]https://aclanthology.org/2021.acl-srw.14/
[tool]https://github.com/mechanicpanic/Chat-Corpora-Annotator
[data]https://github.com/mechanicpanic/Situation_Dataset
[pdf]https://arxiv.org/abs/2106.08556
[pdf]https://arxiv.org/abs/2010.10044
[pdf]https://ieeexplore.ieee.org/document/9414547
[pdf]https://arxiv.org/abs/2106.03337
[pdf]https://arxiv.org/abs/2105.14064
[code]https://github.com/salesforce/ConvSumm
[pdf]https://arxiv.org/abs/2104.08400
[code]https://github.com/GT-SALT/Structure-Aware-BART
[pdf]https://aclanthology.org/2021.tacl-1.88/
[pdf]https://www.aclweb.org/anthology/2020.coling-main.39/
[pdf]https://arxiv.org/abs/2010.01672
[code]https://github.com/GT-SALT/Multi-View-Seq2Seq
[pdf]https://arxiv.org/abs/1911.12237
[data]https://arxiv.org/src/1911.12237v2/anc/corpus.7z
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#medical-dialogue-summarization
[pdf]https://dl.acm.org/doi/abs/10.1145/3534678.3539116
[pdf]https://arxiv.org/abs/2206.03886
http://mpathic.ai/http://mpathic.ai/
[pdf]https://arxiv.org/abs/2111.07564
[pdf]https://www.cs.cmu.edu/~mgormley/papers/zhang+al.emnlp.2021.pdf
[pdf1]https://aclanthology.org/2021.nlpmc-1.9/
[pdf2]https://arxiv.org/abs/2110.07356
[pdf]https://aclanthology.org/2021.acl-long.384/
[code]https://github.com/acmi-lab/modular-summarization
[pdf]https://www.aclweb.org/anthology/2020.coling-main.63/
[code]https://github.com/cuhksz-nlp/HET-MC
[bib]https://www.aclweb.org/anthology/2020.coling-main.63.bib
[pdf]https://arxiv.org/abs/2009.08666
[bib]https://www.aclweb.org/anthology/2020.findings-emnlp.335.bib
[pdf]https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7225507/
[bib]https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers/blob/main
[pdf]https://www.aclweb.org/anthology/2020.nlpmc-1.4/
[bib]https://www.aclweb.org/anthology/2020.nlpmc-1.4.bib
[pdf]https://www.aclweb.org/anthology/W19-1918/
[bib]https://www.aclweb.org/anthology/W19-1918.bib
[pdf]https://www.aclweb.org/anthology/2020.lrec-1.52/
[bib]https://www.aclweb.org/anthology/2020.lrec-1.52.bib
[pdf]https://arxiv.org/abs/1910.01335
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#customer-service-summarization
[pdf]https://aclanthology.org/2022.acl-long.182/
[code]https://github.com/xiaolinandy/rods
[pdf]https://arxiv.org/abs/2204.12951
[pdf]https://arxiv.org/abs/2203.15590
[pdf]https://arxiv.org/abs/2203.01552
[code]https://github.com/jshin49/ds2
[pdf]https://aclanthology.org/2021.findings-emnlp.24/
[data]https://github.com/guyfe/Tweetsumm
[pdf]https://ieeexplore.ieee.org/document/9645319/authors#authors
[pdf]https://arxiv.org/abs/2110.12680
[pdf]https://arxiv.org/abs/2108.13139
[data]https://github.com/xiaolinAndy/CSDS
[pdf]https://dl.acm.org/doi/10.1145/3404835.3463046
[pdf]https://arxiv.org/abs/2009.06851
[pdf]https://arxiv.org/abs/2012.07311
[code]https://github.com/RowitZou/topic-dialog-summ
[pdf]https://arxiv.org/abs/2012.07300
[code]https://github.com/RowitZou/RankAE
[pdf]https://arxiv.org/abs/1910.00825
[pdf]https://dl.acm.org/doi/10.1145/3292500.3330683
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#domain-adaption
[pdf]https://arxiv.org/abs/2212.10018
[pdf]https://arxiv.org/abs/2204.04362
[code]https://github.com/Zeng-WH/DOP-Tuning
[pdf]https://arxiv.org/abs/2103.11332
[code]https://github.com/TysonYu/AdaptSum
[pdf]https://www.aclweb.org/anthology/W10-2603/
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#others
[pdf]https://aclanthology.org/2022.emnlp-main.250/
[code]https://github.com/megagonlabs/qa-summarization
[pdf]https://aclanthology.org/2022.emnlp-main.72/
[pdf]https://arxiv.org/abs/2212.12652
[pdf]https://arxiv.org/abs/2301.12376
[code]https://github.com/xnliang98/bart-glc
[pdf]https://arxiv.org/abs/2301.10483
[code]https://github.com/amazon-science/AWS-SWING
[pdf]https://arxiv.org/abs/2212.09750
[pdf]https://arxiv.org/pdf/2211.08464.pdf
[pdf]https://arxiv.org/abs/2211.07145
[pdf]https://aclanthology.org/2022.emnlp-main.325/
[code]https://github.com/BinWang28/FacEval
[pdf]https://arxiv.org/abs/2210.09894
[pdf]https://arxiv.org/abs/2210.09474
[pdf]https://aclanthology.org/2022.coling-1.569/
[pdf]https://aclanthology.org/2022.coling-1.531/
[code]https://github.com/shakeley/View2dSum
[pdf]https://aclanthology.org/2022.coling-1.528/
[pdf]https://arxiv.org/abs/2202.05599
[code]https://github.com/krystalan/ClidSum
[pdf]https://arxiv.org/abs/2209.11910
[pdf]https://arxiv.org/abs/2208.03898
[pdf]https://arxiv.org/abs/2207.08305
[pdf]https://aclanthology.org/2022.findings-naacl.53/
[pdf]https://aclanthology.org/2022.naacl-industry.6/
[pdf]https://aclanthology.org/2022.naacl-srw.32/
[code]https://github.com/dafraile/Clinical-Dialogue-Summarization
[pdf]https://aclanthology.org/2022.naacl-main.418/
[code]https://github.com/kite99520/DialSummEval
[pdf]https://aclanthology.org/2022.naacl-main.357/
[code]https://github.com/Zeng-WH/DOP-Tuning
[pdf]https://aclanthology.org/2022.naacl-main.283/
[pdf]https://arxiv.org/abs/2205.13108
[code]https://github.com/seongminp/graph-dialogue-summary
[pdf]https://aclanthology.org/2022.dialdoc-1.1/
[data]https://github.com/xcfcode/MSAMSum
[pdf]https://arxiv.org/abs/2205.00379
[pdf]https://aclanthology.org/2022.findings-naacl.125/
[code]https://github.com/JiaQiSJTU/DialSent-PGG
[pdf]https://arxiv.org/abs/2112.08713
[pdf]https://aclanthology.org/2021.newsum-1.12/
[pdf]https://arxiv.org/abs/2111.03284
[pdf]https://www.akbc.ws/2021/papers/AJKd0iIFMDc
[code]https://github.com/HKUST-KnowComp/CODC-Dialogue-Summarization
[pdf]https://www.techrxiv.org/articles/preprint/Prompt_scoring_system_for_dialogue_summarization_using_GPT-3/16652392
[pdf]https://www.cc.gatech.edu/~dyang888/docs/emnlp21_chen_coda.pdf
[code]https://github.com/GT-SALT/CODA
[pdf]https://arxiv.org/abs/2109.08232
[pdf]https://arxiv.org/abs/2108.09597
[pdf]https://aclanthology.org/2021.acl-long.471/
[code]https://github.com/xiyan524/RepSum
[pdf]https://aclanthology.org/2021.acl-long.117/
[code]https://github.com/xcfcode/PLM_annotator
[pdf]https://arxiv.org/abs/2011.08291
[pdf]https://arxiv.org/abs/2104.07545
[code]https://github.com/birch-research/hierarchical-learning
[pdf]https://arxiv.org/abs/2103.13587
[code]https://github.com/sansiri20/forums_summ
[pdf]https://arxiv.org/abs/2103.10599
[pdf]https://dl.acm.org/doi/10.1145/3357384.3357940
[pdf]https://dl.acm.org/doi/10.1145/3159652.3160588
[pdf]https://arxiv.org/abs/1711.00092
[pdf]https://www.aclweb.org/anthology/W16-3605/
[pdf]https://www.aclweb.org/anthology/C04-1110/
[bib]https://www.aclweb.org/anthology/C04-1110.bib
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#long-document
[pdf]https://arxiv.org/abs/2303.14337
[code]https://github.com/blender-nlp/SmartBook
[pdf]https://arxiv.org/abs/2301.13298
[code]https://github.com/martiansideofthemoon/longeval-summarization
[pdf]https://arxiv.org/abs/2301.11312
[code]https://github.com/recitalAI/loralay-datasets
[pdf]https://arxiv.org/abs/2211.10247
[pdf]https://arxiv.org/abs/2211.04903
[pdf]https://arxiv.org/abs/2210.16732
[code]https://github.com/huankoh/How-Far-are-We-from-Robust-Long-Abstractive-Summarization
[pdf]https://aclanthology.org/2022.emnlp-main.8/
[code]https://github.com/tencent-ailab/Lodoss
[pdf]https://aclanthology.org/2022.coling-1.545/
[code]https://github.com/tuananhphan97vn/HeterGraphLongSum
[pdf]https://aclanthology.org/2022.coling-1.512/
[code]https://github.com/dungdx34/MTGNN-SUM
[pdf]https://arxiv.org/abs/2210.04126
[pdf]https://aclanthology.org/2022.coling-1.546/
[code]https://github.com/xashely/GRETEL_extractive
[pdf]https://arxiv.org/abs/2208.09454
[pdf]https://aclanthology.org/2022.coling-1.558/
[code]https://github.com/xnliang98/c2f-far
[pdf]https://arxiv.org/abs/2208.04347
[code]https://github.com/google-research/pegasus/tree/main/pegasus/flax
[pdf]https://arxiv.org/abs/2207.00939
[pdf]https://aclanthology.org/2022.acl-long.450/
[code]https://github.com/nianlonggu/memsum
[pdf]https://www.aaai.org/AAAI22Papers/AAAI-3882.MoroG.pdf
[pdf]https://arxiv.org/abs/2205.12486
[pdf]https://arxiv.org/abs/2205.12476
[pdf]https://aclanthology.org/2022.emnlp-main.29/
[data]https://github.com/tagoyal/snac
[pdf]https://arxiv.org/abs/2204.03301
[pdf]https://arxiv.org/abs/2203.15349
[data1]https://huggingface.co/datasets/midas/ldkp3k
[data2]https://huggingface.co/datasets/midas/ldkp10k
[pdf]https://aclanthology.org/2022.acl-long.58/
[code]https://github.com/ShuyangCao/hibrids_summ
[data]https://gov-report-data.github.io/
[pdf]https://arxiv.org/abs/2203.09629
[code]https://github.com/QianRuan/histruct
[pdf]https://arxiv.org/abs/2203.07586
[pdf]https://arxiv.org/abs/2110.10150
[pdf]https://aclanthology.org/2022.acl-long.118/
[code]https://github.com/Yale-LILY/DYLE
[pdf]https://arxiv.org/abs/2201.08495
[code]https://github.com/atharsefid/SciBERTSUM
[pdf]https://arxiv.org/abs/2112.08550
[pdf]https://arxiv.org/abs/2112.07916
[pdf]https://arxiv.org/abs/2112.01660
[pdf]https://arxiv.org/abs/2110.07850
[pdf]https://arxiv.org/abs/2110.01280
[pdf]https://arxiv.org/abs/2109.14059
[pdf]https://arxiv.org/abs/2109.03888
[code]https://github.com/potsawee/encdec_attn_sparse
[pdf]https://aclanthology.org/2021.acl-short.137/
[data]https://github.com/hfthair/emerald_crawler
[pdf]https://www.aclweb.org/anthology/2021.naacl-main.470/
[code]https://github.com/pcui-nlp/SSN_DM
[pdf]https://aclanthology.org/2021.acl-long.470/
[pdf]https://www.aclweb.org/anthology/2021.eacl-main.154/
[pdf]https://www.aclweb.org/anthology/2021.eacl-main.93/
[code]https://github.com/mirandrom/HipoRank
[pdf]https://arxiv.org/abs/2104.03057
[code]https://github.com/ChenxinAn-fdu/CGSum
[pdf]https://arxiv.org/abs/2104.02112
[code]https://github.com/luyang-huang96/LongDocSum
[data]https://gov-report-data.github.io/
[pdf]https://arxiv.org/abs/2102.00176
[code]https://github.com/neulab/ReviewAdvisor
[pdf]https://aclanthology.org/2021.acl-srw.7/
[pdf]https://arxiv.org/abs/2101.03553
[pdf]https://arxiv.org/abs/2012.14136
[code]https://github.com/Georgetown-IR-Lab/ExtendedSumm
[pdf]https://arxiv.org/abs/2012.11213
pdfhttps://www.aclweb.org/anthology/2020.aacl-main.51/
[code]http://www.cs.ubc.ca/cs-research/lci/research-groups/natural-language-processing/
[pdf]https://arxiv.org/abs/1909.03186
[pdf]https://arxiv.org/abs/2010.09252
[code]https://github.com/TysonYu/Laysumm
[pdf]https://arxiv.org/abs/2010.09190
[code]https://github.com/mingzi151/SummPip
[pdf]https://arxiv.org/abs/2010.06253
[pdf]https://arxiv.org/abs/2010.14235
[data]https://github.com/yaolu/Multi-XScience
[pdf]https://arxiv.org/abs/2004.06190
[pdf]https://arxiv.org/abs/2004.15011
[data]https://github.com/allenai/scitldr
[pdf]https://arxiv.org/abs/1909.08089
[code]https://github.com/Wendy-Xiao/Extsumm_local_global_context
[pdf]https://arxiv.org/abs/1909.01716
[data]https://cs.stanford.edu/~myasu/projects/scisumm_net/
[pdf]https://www.aclweb.org/anthology/P19-1204/
[data]https://github.com/levguy/talksumm
[pdf]https://arxiv.org/abs/1804.05685
[data]https://github.com/armancohan/long-summarization
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#factual-consistency
https://camo.githubusercontent.com/78e793cfc7f09f7a1ef017842f733cb02751d00180c985c3293bd66d1172064e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f486f77253230746f2532306576616c756174652532306661637475616c253230636f6e73697374656e63792532306f6625323073756d6d6172792d6576616c756174696f6e2d627269676874677265656e
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factsummhttps://github.com/Huffon/factsumm
[pdf]https://arxiv.org/abs/2303.15621
[pdf]https://arxiv.org/abs/2303.03948
[pdf]https://arxiv.org/abs/2303.03278
[code]https://github.com/amazon-science/faithful-summarization-generation
[pdf]https://arxiv.org/abs/2303.00242
[code]https://github.com/google-research/language/tree/master/language/diffqg
[pdf]https://aclanthology.org/2022.emnlp-main.816/
[code]https://github.com/COFE2022/CoFE
[pdf]https://aclanthology.org/2022.emnlp-main.663/
[code]https://github.com/mcao516/rej-summ
[pdf]https://aclanthology.org/2022.emnlp-main.478/
[pdf]https://arxiv.org/abs/2301.13298
[code]https://github.com/martiansideofthemoon/longeval-summarization
[pdf]https://arxiv.org/abs/2212.10622
[pdf]https://arxiv.org/abs/2212.09726
[pdf]https://arxiv.org/abs/2212.02712
[pdf]https://arxiv.org/abs/2211.16853
[pdf]https://arxiv.org/abs/2211.12118
[pdf]https://arxiv.org/pdf/2211.08464.pdf
[pdf]https://arxiv.org/abs/2211.08412
[pdf]https://arxiv.org/abs/2211.06196
[code]https://github.com/salesforce/CompEdit
[pdf]https://arxiv.org/abs/2211.02580
[code]https://github.com/meetdavidwan/faithful-multimodal-summ
[pdf]https://arxiv.org/abs/2211.00294
[pdf]https://arxiv.org/abs/2210.17378
[data]https://github.com/YanzhuGuo/SummFC
[pdf]https://arxiv.org/abs/2210.13210
[code]https://github.com/VanderpoelLiam/CPMI
[pdf]https://arxiv.org/abs/2210.12378
[code]https://github.com/vidhishanair/FactEdit
[pdf]https://aclanthology.org/2022.coling-1.537/
[code]https://github.com/taka2946/sumphrase
[pdf]https://arxiv.org/abs/2210.02804
[pdf]https://arxiv.org/abs/2209.03549
[code]https://github.com/ZhangShiyue/extractive_is_not_faithful
[pdf]https://arxiv.org/abs/2209.03479
[code]https://github.com/Wendy-Xiao/Entity-based-SpanCopy
[pdf]https://aclanthology.org/2022.findings-naacl.180/
[pdf]https://aclanthology.org/2022.findings-naacl.76/
[code]https://github.com/hwanheelee1993/MFMA
https://github.com/hwanheelee1993/MFMAhttps://github.com/hwanheelee1993/MFMA
[pdf]https://aclanthology.org/2022.findings-naacl.40/
[code]https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers/blob/main
[pdf]https://aclanthology.org/2022.naacl-main.74/
[code]https://github.com/meetdavidwan/factpegasus
[pdf]https://arxiv.org/abs/2207.02263
[pdf]https://aclanthology.org/2022.tacl-1.10/
[code]https://github.com/tingofurro/summac/
[pdf]https://aclanthology.org/2022.acl-long.236/
[code]https://github.com/mcao516/entfa
[pdf]https://arxiv.org/abs/2205.12854
[code]https://github.com/Liyan06/AggreFact
[pdf]https://aclanthology.org/2022.naacl-main.199/
[code]https://github.com/joshbambrick/Falsesum
[pdf]https://arxiv.org/abs/2205.02035
[code]https://github.com/hwanheelee1993/MFMA
[pdf]https://arxiv.org/abs/2204.13761
[pdf]https://arxiv.org/abs/2204.10290
[code]https://github.com/amazon-research/summary-reference-revision
[pdf]https://arxiv.org/abs/2204.08263
[pdf]https://arxiv.org/abs/2204.07562
[code]https://github.com/AshOlogn/Evaluating-Factuality-in-Text-Simplification
[pdf]https://aclanthology.org/2022.naacl-main.236/
[code]https://github.com/amazon-research/fact-graph
[pdf]https://arxiv.org/abs/2203.08436
[code]https://github.com/allenai/pinocchio
[pdf]https://aclanthology.org/2022.naacl-main.415/
[pdf]https://aclanthology.org/2022.naacl-main.187/
[code]https://github.com/salesforce/QAFactEval
[pdf]https://arxiv.org/abs/2112.01147
[pdf]https://aclanthology.org/2021.findings-emnlp.179/
[code]https://github.com/zide05/AdvFact
[pdf]https://arxiv.org/abs/2111.09525
[code]https://github.com/tingofurro/summac/
[pdf]https://arxiv.org/abs/2111.03284
[pdf]https://aclanthology.org/2021.emnlp-main.9/
[pdf]https://arxiv.org/abs/2110.07166
[pdf]https://aclanthology.org/2022.naacl-main.417/
[pdf]https://arxiv.org/abs/2109.10650
[data]https://github.com/XinnuoXu/MiRANews
[pdf]https://arxiv.org/abs/2109.09784
[pdf]https://arxiv.org/abs/2109.09209
[code]https://shuyangcao.github.io/projects/cliff_summ
[pdf]https://aclanthology.org/2022.acl-long.100/
[code]https://github.com/fladhak/effective-faithfulness
[pdf]https://arxiv.org/abs/2108.13134
[code]https://github.com/xieyxclack/factual_coco
[pdf]https://aclanthology.org/2021.ecnlp-1.19/
[pdf]https://arxiv.org/abs/2106.02278
[data]https://github.com/google-research-datasets/AgreeSum
[pdf]https://aclanthology.org/2021.acl-long.474/
[pdf]https://aclanthology.org/2021.acl-long.536/
[code]https://github.com/amazon-research/fact-check-summarization
[pdf]https://www.aclweb.org/anthology/2021.eacl-main.34/
[code]https://github.com/skgabriel/coopnet
[pdf]https://arxiv.org/abs/2104.09061
[code]https://github.com/CogComp/faithful_summarization
https://camo.githubusercontent.com/7b91995d6055cc5c0cdbe290210583e14208f9250923c78388e3c3b3a1941854/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d696d70726f76652d6f72616e6765
[pdf]https://arxiv.org/abs/2104.13346
[code]https://github.com/artidoro/frank
https://camo.githubusercontent.com/24cd0b763396c976c32ddb3c711fa1b724f09ff1174f5cd0ef8d9f4868e31cfe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6576616c756174696f6e2d627269676874677265656e
[pdf]https://arxiv.org/abs/2104.04302
[code]https://github.com/tagoyal/factuality-datasets
[pdf]https://arxiv.org/abs/2103.12693
[code]https://github.com/recitalAI/QuestEval
https://camo.githubusercontent.com/24cd0b763396c976c32ddb3c711fa1b724f09ff1174f5cd0ef8d9f4868e31cfe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6576616c756174696f6e2d627269676874677265656e
[pdf]https://arxiv.org/abs/2003.08612
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[pdf]https://www.aclweb.org/anthology/2021.eacl-main.235/
[code]https://github.com/amazon-research/fact-check-summarization
https://camo.githubusercontent.com/24cd0b763396c976c32ddb3c711fa1b724f09ff1174f5cd0ef8d9f4868e31cfe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6576616c756174696f6e2d627269676874677265656e
[pdf]https://www.aclweb.org/anthology/2020.coling-main.502/
[code]https://github.com/ypnlp/coling
https://camo.githubusercontent.com/7b91995d6055cc5c0cdbe290210583e14208f9250923c78388e3c3b3a1941854/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d696d70726f76652d6f72616e6765
[pdf]https://arxiv.org/abs/2011.13662
[code]https://github.com/fajri91/ffci
https://camo.githubusercontent.com/24cd0b763396c976c32ddb3c711fa1b724f09ff1174f5cd0ef8d9f4868e31cfe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6576616c756174696f6e2d627269676874677265656e
[pdf]https://arxiv.org/abs/2010.08014
[code]https://github.com/neulab/guided_summarization
https://camo.githubusercontent.com/7b91995d6055cc5c0cdbe290210583e14208f9250923c78388e3c3b3a1941854/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d696d70726f76652d6f72616e6765
[pdf]https://www.aclweb.org/anthology/2020.eval4nlp-1.1/
[pdf]https://arxiv.org/abs/2011.02593
[code]https://github.com/violet-zct/fairseq-detect-hallucination
[pdf]https://arxiv.org/abs/2010.12834
https://camo.githubusercontent.com/24cd0b763396c976c32ddb3c711fa1b724f09ff1174f5cd0ef8d9f4868e31cfe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6576616c756174696f6e2d627269676874677265656e
[pdf]https://arxiv.org/abs/2010.12723
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[pdf]https://arxiv.org/abs/2010.08712
[code]https://github.com/mcao610/Factual-Error-Correction
https://camo.githubusercontent.com/6efef5908ccede34b5799849580a80625d3cf784c01a7b5d4449b75a906b8302/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d636f72726563742d726564
[pdf]https://arxiv.org/abs/2010.02443
https://camo.githubusercontent.com/6efef5908ccede34b5799849580a80625d3cf784c01a7b5d4449b75a906b8302/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d636f72726563742d726564
[pdf]https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers/blob/main
https://camo.githubusercontent.com/6efef5908ccede34b5799849580a80625d3cf784c01a7b5d4449b75a906b8302/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d636f72726563742d726564
[pdf]https://arxiv.org/abs/1910.12840
[code]https://github.com/salesforce/factCC
https://camo.githubusercontent.com/24cd0b763396c976c32ddb3c711fa1b724f09ff1174f5cd0ef8d9f4868e31cfe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6576616c756174696f6e2d627269676874677265656e
[pdf]https://arxiv.org/abs/2009.13312
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[pdf]https://arxiv.org/abs/2005.00661
[data]https://github.com/google-research-datasets/xsum_hallucination_annotations
https://camo.githubusercontent.com/9cae165c27990b425f8df0281b24835177b6bd3fb66adc085cb5d03352785871/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d616e616c797369732d626c7565
[pdf]https://arxiv.org/abs/2005.00882
https://camo.githubusercontent.com/7b91995d6055cc5c0cdbe290210583e14208f9250923c78388e3c3b3a1941854/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d696d70726f76652d6f72616e6765
[pdf]https://arxiv.org/abs/1911.02541
https://camo.githubusercontent.com/7b91995d6055cc5c0cdbe290210583e14208f9250923c78388e3c3b3a1941854/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d696d70726f76652d6f72616e6765
[pdf]https://arxiv.org/abs/2005.03754
[code]https://github.com/esdurmus/feqa
https://camo.githubusercontent.com/24cd0b763396c976c32ddb3c711fa1b724f09ff1174f5cd0ef8d9f4868e31cfe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6576616c756174696f6e2d627269676874677265656e
[pdf]https://arxiv.org/abs/2004.04228
[code]https://github.com/W4ngatang/qags
https://camo.githubusercontent.com/24cd0b763396c976c32ddb3c711fa1b724f09ff1174f5cd0ef8d9f4868e31cfe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6576616c756174696f6e2d627269676874677265656e
[pdf]https://arxiv.org/abs/2005.01159
https://camo.githubusercontent.com/7b91995d6055cc5c0cdbe290210583e14208f9250923c78388e3c3b3a1941854/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d696d70726f76652d6f72616e6765
[pdf]https://arxiv.org/abs/2006.15435
https://camo.githubusercontent.com/7b91995d6055cc5c0cdbe290210583e14208f9250923c78388e3c3b3a1941854/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d696d70726f76652d6f72616e6765
[pdf]https://arxiv.org/abs/1905.13322
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[pdf]https://www.aclweb.org/anthology/P19-1213/
[data]https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2002
https://camo.githubusercontent.com/24cd0b763396c976c32ddb3c711fa1b724f09ff1174f5cd0ef8d9f4868e31cfe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d6576616c756174696f6e2d627269676874677265656e
[pdf]https://www.aclweb.org/anthology/C18-1121/
[code]https://github.com/hrlinlp/entail_sum
https://camo.githubusercontent.com/7b91995d6055cc5c0cdbe290210583e14208f9250923c78388e3c3b3a1941854/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d696d70726f76652d6f72616e6765
[pdf]https://arxiv.org/abs/1711.04434
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[pdf]https://www.sciencedirect.com/science/article/abs/pii/S0306457320309675
https://camo.githubusercontent.com/7b91995d6055cc5c0cdbe290210583e14208f9250923c78388e3c3b3a1941854/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d696d70726f76652d6f72616e6765
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#contrastive-learning
[pdf]https://aclanthology.org/2022.coling-1.508/
[code]https://github.com/ChenxinAn-fdu/CoLo
[pdf]https://arxiv.org/abs/2109.09209
[code]https://shuyangcao.github.io/projects/cliff_summ
https://arxiv.org/abs/2109.03481https://arxiv.org/abs/2109.03481
[pdf]https://arxiv.org/abs/2108.11992
[code]https://github.com/chz816/esacl
[pdf]https://arxiv.org/abs/2104.05094
[pdf]https://arxiv.org/abs/2108.11846
[code]https://github.com/ShichaoSun/ConAbsSum
[pdf]https://aclanthology.org/2021.acl-short.135/
[code]https://github.com/yixinL7/SimCLS
[pdf]https://arxiv.org/abs/2012.07280
[pdf]https://arxiv.org/abs/1811.02394
[code]https://github.com/lliangchenc/DeepChannel
[pdf]https://arxiv.org/abs/2010.01781
[code]https://github.com/whl97/LS-Score
[pdf]https://www.aclweb.org/anthology/D19-1301/
[code]https://github.com/travel-go/Abstractive-Text-Summarization
https://patch-diff.githubusercontent.com/xcfcode/Summarization-Papers#evaluation
[pdf]https://arxiv.org/abs/2303.15621
[pdf]https://arxiv.org/abs/2303.15078
[pdf]https://arxiv.org/abs/2302.04166
[code]https://github.com/jinlanfu/GPTScore
[pdf]https://arxiv.org/abs/2212.10397
[pdf]https://arxiv.org/abs/2212.10013
[pdf]https://arxiv.org/abs/2212.08775
[code]https://github.com/google-research/google-research/tree/master/rise
[pdf]https://arxiv.org/abs/2210.14260
[pdf]https://arxiv.org/abs/2210.08145
[pdf]https://aclanthology.org/2022.coling-1.527/
[code]https://github.com/julmaxi/summary_coherence_evaluation
[pdf]https://aclanthology.org/2022.coling-1.515/
[code]https://github.com/NKWBTB/PrefScore
[pdf]https://arxiv.org/abs/2209.06517
[code]https://github.com/julmaxi/summary_coherence_evaluation
[pdf]https://arxiv.org/abs/2207.04660
[pdf]https://aclanthology.org/2022.naacl-main.173/
[code]https://github.com/PrimerAI/primer-research/
https://github.com/PrimerAI/primer-research/https://github.com/PrimerAI/primer-research/
[pdf]https://aclanthology.org/2022.naacl-main.442/
[code]https://cogcomp.seas.upenn.edu/page/publication_view/973
[pdf]https://aclanthology.org/2022.naacl-main.175/
[code]https://github.com/forrestbao/SueNes/
[pdf]https://aclanthology.org/2022.naacl-main.153/
[code]https://github.com/YizhuLiu/summeval
[pdf]https://arxiv.org/abs/2205.12394
[code]https://github.com/YuLuLiu/MaskEval
[pdf]https://arxiv.org/abs/2204.04991
[pdf]https://arxiv.org/abs/2103.10918
[code]https://github.com/PrimerAI/blanc/tree/master/shannon
[pdf]https://arxiv.org/abs/2202.04003
[code]https://github.com/zhuyunqi96/ngramObj
[pdf]https://arxiv.org/abs/2201.11176
[code]https://github.com/AIPHES/DiscoScore
[pdf]https://arxiv.org/abs/2201.09282
[code]https://github.com/Raghav10j/WIDAR
[pdf]https://arxiv.org/abs/2112.01589
[pdf]https://aclanthology.org/2021.newsum-1.6/
[pdf]https://arxiv.org/abs/2110.05847
[pdf]https://arxiv.org/abs/2110.04384
[pdf]https://arxiv.org/abs/2109.11503
[code]https://github.com/ZhangShiyue/Lite2-3Pyramid
[pdf]https://arxiv.org/abs/2103.12693
[code]https://github.com/recitalAI/QuestEval
[pdf]https://arxiv.org/abs/2106.11520
[code]https://github.com/neulab/BARTScore
[pdf]https://aclanthology.org/2021.acl-long.34/
[code]https://github.com/Chen-Wang-CUHK/Training-Free-and-Ref-Free-Summ-Evaluation
[pdf]https://arxiv.org/abs/2106.01478
[pdf]https://arxiv.org/abs/2106.00219
[pdf]https://arxiv.org/abs/2105.06027
[pdf]https://www.aclweb.org/anthology/2021.humeval-1.10/
[code]https://github.com/nesliskender/reliability_humeval_summarization
[pdf]https://aclanthology.org/2021.acl-demo.18/
[data]https://github.com/robustness-gym/summvis
[pdf]https://arxiv.org/abs/2012.14602
[pdf]https://www.aclweb.org/anthology/2021.eacl-main.160/
[code]https://github.com/julmaxi/summary_lq_analysis
[pdf]https://www.aclweb.org/anthology/2020.coling-main.498/
[bib]https://www.aclweb.org/anthology/2020.coling-main.498.bib
[pdf]https://arxiv.org/abs/2011.13662
[code]https://github.com/fajri91/ffci
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[pdf]https://arxiv.org/abs/2007.12626
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[pdf]https://arxiv.org/abs/2203.10254
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[pdf]https://aclanthology.org/2022.acl-long.351/
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[pdf]https://arxiv.org/abs/2110.12645
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[pdf]https://arxiv.org/abs/2110.11207
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[code]https://github.com/amazon-research/BartGraphSumm
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[pdf]https://arxiv.org/abs/2005.03724
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[pdf]https://arxiv.org/abs/2005.10043
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[pdf]https://arxiv.org/abs/2001.09386
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[code]https://github.com/ucfnlp/multidoc_summarization
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[pdf]https://arxiv.org/abs/1611.09238
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[pdf]https://www.microsoft.com/en-us/research/publication/event-centric-summary-generation/
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[pdf]https://arxiv.org/abs/2110.07936
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[pdf]https://link.springer.com/chapter/10.1007/978-3-030-14799-0_17
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[pdf]https://aclanthology.org/2021.findings-emnlp.273/
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[pdf]https://aclanthology.org/2021.emnlp-main.741/
[code]https://github.com/alirezasalemi7/ARMAN
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[pdf]https://arxiv.org/abs/2107.02137
[pdf]https://arxiv.org/abs/2012.15525
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[pdf]https://arxiv.org/abs/2011.09739
[code]https://github.com/Ruifeng-paper/FactExsum-coling2020
[pdf]https://www.aclweb.org/anthology/2020.findings-emnlp.289/
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[pdf]https://arxiv.org/abs/2010.12836
[pdf]https://arxiv.org/abs/2010.13002
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[pdf]https://arxiv.org/abs/2004.11026
[pdf]https://arxiv.org/abs/1912.08777
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[pdf]https://arxiv.org/abs/2003.13028
[pdf]https://arxiv.org/abs/2002.07767
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[pdf]https://arxiv.org/abs/1908.08345
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[pdf]https://arxiv.org/abs/1905.02450
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