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Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly.
Best-Worst Scaling: A Model for the Largest Difference Judgments
Jordan J Louviere and George G Woodworth. 1991 · 1991
Earlier work this paper cites.
Generating summaries of multiple news articles
Kathleen R. McKeown and Dragomir R. Radev. 1995 · 1995
Earlier work this paper cites.
The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries
Jaime Carbonell and Jade Goldstein. 1998 · 1998
Earlier work this paper cites.
Generating Natural Language Summaries from Multiple On-Line Sources
Dragomir R. Radev and Kathleen R. McKeown. 1998 · 1998
Earlier work this paper cites.
Information Fusion in the Context of Multi-Document Summarization
Regina Barzilay, Kathleen R. McKeown, and Michael Elhadad. 1999 · 1999
Earlier work this paper cites.
Centroid-Based Summarization of Multiple Documents: Sentence Extraction utility-based evaluation, and user studies
Dragomir R. Radev, Hongyan Jing, and Malgorzata Budzikowska. 2000 · 2000
Earlier work this paper cites.
Lexrank: Graph-Based Lexical Centrality as Salience in Text Summarization
Günes Erkan and Dragomir R Radev. 2004 · 2004
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Rouge: A Package for Automatic Evaluation of Summaries
Chin-Yew Lin. 2004 · 2004
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Textrank: Bringing Order into Text
Rada Mihalcea and Paul Tarau. 2004 · 2004
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An Introduction to DUC-2004
Over Paul and Yen James. 2004 · 2004
Earlier work this paper cites.
The New York Times Annotated Corpus
2008 · 2008
Earlier work this paper cites.
Exploring Content Models for Multi-Document Summarization
Aria Haghighi and Lucy Vanderwende. 2009 · 2009
Earlier work this paper cites.
Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions
Kavita Ganesan, ChengXiang Zhai, and Jiawei Han. 2010 · 2010
Earlier work this paper cites.
Overview of the TAC 2011 Summarization Track: Guided Task and AESOP Task
Karolina Owczarzak and Hoa Trang Dang. 2011 · 2011
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Annotated Gigaword
Courtney Napoles, Matthew R. Gormley, and Benjamin Van Durme. 2012 · 2012
Earlier work this paper cites.
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
A repository of state of the art and competitive baseline summaries for generic news summarization
Kai Hong, John M. Conroy, Benoît Favre, Alex Kulesza, Hui Lin, and Ani Nenkova. 2014 · 2014
Cited alongside, same era.
Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Cited alongside, same era.
Best-Worst Scaling: Theory, Methods and Applications
Jordan Louviere, Terry Flynn, and A. A. J. Marley. 2015 · 2015
Cited alongside, same era.
A Neural Attention Model for Abstractive Sentence Summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Cited alongside, same era.
Tal Baumel, Matan Eyal, and Michael Elhadad. 2018 · 2018
Later among the works it cites.
Deep Communicating Agents for Abstractive Summarization
Asli Çelikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi. 2018 · 2018
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A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018 · 2018
Later among the works it cites.
Bottom-Up Abstractive Summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander M. Rush. 2018 · 2018
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Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies
Max Grusky, Mor Naaman, and Yoav Artzi. 2018 · 2018
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Neural Summarization by Extracting Sentences and Words
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
Cited alongside, same era.
Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
Sumit Chopra, Michael Auli, and Alexander M. Rush. 2016 · 2016
Cited alongside, same era.
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
Ramesh Nallapati, Bowen Zhou, Cícero Nogueira dos Santos, Çaglar Gülçehre, and Bing Xiang. 2016b · 2016
Cited alongside, same era.
Improving Multi-Document Summarization via Text Classification
Ziqiang Cao, Wenjie Li, Sujian Li, and Furu Wei. 2017 · 2017
Cited alongside, same era.
Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation
Svetlana Kiritchenko and Saif Mohammad. 2017 · 2017
Cited alongside, same era.
A Deep Reinforced Model for Abstractive Summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Get To The Point: Summarization with Pointer-Generator Networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
Cited alongside, same era.
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document summarization
Logan Lebanoff, Kaiqiang Song, and Fei Liu. 2018 · 2018
Later among the works it cites.
Generating Wikipedia by Summarizing Long Sequences
Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. 2018 · 2018
Later among the works it cites.
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018a · 2018
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Ranking Sentences for Extractive Summarization with Reinforcement Learning
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018b · 2018
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Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study
Jianmin Zhang, Jiwei Tan, and Xiaojun Wan. 2018a · 2018
Later among the works it cites.
Auto-hmds: Automatic Construction of a Large Heterogeneous Multilingual Multi-Document Summarization Corpus
Markus Zopf. 2018 · 2018
Later among the works it cites.
Adaptive input representations for neural language modeling
Alexei Baevski and Michael Auli. 2019 · 2019
Closest in time.
MeanSum: A neural model for unsupervised multi-document abstractive summarization
Eric Chu and Peter Liu. 2019 · 2019
Closest in time.
Transformer-XL: Language modeling with longer-term dependency
Zihang Dai, Zhilin Yang, Yiming Yang, William W. Cohen, Jaime Carbonell, Quoc V. Le, and Ruslan Salakhutdinov. 2019 · 2019
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