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Generating a text abstract from a set of documents remains a challenging task.
Long short-term memory
Sepp Hochreiter and Jurgen Schmidhuber. 1997 · 1997
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.
Information fusion in the context of multi-document summarization
Regina Barzilay, Kathleen R. McKeown, and Michael Elhadad. 1999 · 1999
Earlier work this paper cites.
The decomposition of human-written summary sentences
Hongyan Jing and Kathleen McKeown. 1999 · 1999
Earlier work this paper cites.
A noisy-channel model for document compression
Hal Daume III and Daniel Marcu. 2002 · 2002
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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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An introduction to DUC-2004
Paul Over and James Yen. 2004 · 2004
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Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion
Lucy Vanderwende, Hisami Suzuki, Chris Brockett, and Ani Nenkova. 2007 · 2007
Earlier work this paper cites.
Multi-candidate reduction: Sentence compression as a tool for document summarization tasks
David Zajic, Bonnie J. Dorr, Jimmy Lin, and Richard Schwartz. 2007 · 2007
Earlier work this paper cites.
Extractive vs. NLG-based abstractive summarization of evaluative text: The effect of corpus controversiality
Giuseppe Carenini and Jackie Chi Kit Cheung. 2008 · 2008
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Overview of the TAC 2008 update summarization task
Hoa Trang Dang and Karolina Owczarzak. 2008 · 2008
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The new york times annotated corpus
Evan Sandhaus. 2008 · 2008
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Extractive summarization using supervised and semi-supervised learning
Kam-Fai Wong, Mingli Wu, and Wenjie Li. 2008 · 2008
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A scalable global model for summarization
Dan Gillick and Benoit Favre. 2009 · 2009
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The ICSI/UTD summarization system at TAC 2009
Dan Gillick, Benoit Favre, Dilek Hakkani-Tur, Berndt Bohnet, Yang Liu, and Shasha Xie. 2009 · 2009
Earlier work this paper cites.
Exploring content models for multi-document summarization
Aria Haghighi and Lucy Vanderwende. 2009 · 2009
Earlier work this paper cites.
An extractive supervised two-stage method for sentence compression
Dimitrios Galanis and Ion Androutsopoulos. 2010 · 2010
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.
Jointly learning to extract and compress
Taylor Berg-Kirkpatrick, Dan Gillick, and Dan Klein. 2011 · 2011
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Automatic summarization
Ani Nenkova and Kathleen McKeown. 2011 · 2011
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Determinantal Point Processes for Machine Learning
Alex Kuleszaand Ben Taskar. 2012 · 2012
Earlier work this paper cites.
Document summarization via guided sentence compression
Chen Li, Fei Liu, Fuliang Weng, and Yang Liu. 2013 · 2013
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Sentence compression with joint structural inference
Kapil Thadani and Kathleen McKeown. 2013 · 2013
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A sentence compression based framework to query-focused multi-document summarization
Lu Wang, Hema Raghavan, Vittorio Castelli, Radu Florian, and Claire Cardie. 2013 · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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A hybrid approach to multi-document summarization of opinions in reviews
Giuseppe Di Fabbrizio, Amanda J. Stent, and Robert Gaizauskas. 2014 · 2014
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Automatic summarization of student course feedback
Wencan Luo, Fei Liu, Zitao Liu, and Diane Litman. 2016 · 2016
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Language as a latent variable: Discrete generative models for sentence compression
Yishu Miao and Phil Blunsom. 2016 · 2016
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Abstractive text summarization using sequence-to-sequence RNNs and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016 · 2016
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Neural headline generation on abstract meaning representation
Sho Takase, Jun Suzuki, Naoaki Okazaki, Tsutomu Hirao, and Masaaki Nagata. 2016 · 2016
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Improving multi-document summarization via text classification
Ziqiang Cao, Wenjie Li, Sujian Li, and Furu Wei. 2017 · 2017
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Extractive summarization using multi-task learning with document classification
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Shima Gerani, Yashar Mehdad, Giuseppe Carenini, Raymond T. Ng, and Bita Nejat. 2014 · 2014
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A repository of state of the art and competitive baseline summaries for generic news summarization
Kai Hong, John M Conroy, Benoit Favre, Alex Kulesza, Hui Lin, and Ani Nenkova. 2014 · 2014
Cited alongside, same era.
Modelling events through memory-based, open-ie patterns for abstractive summarization
Daniele Pighin, Marco Cornolti, Enrique Alfonseca, and Katja Filippova. 2014 · 2014
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Abstractive multi-document summarization via phrase selection and merging
Lidong Bing, Piji Li, Yi Liao, Wai Lam, Weiwei Guo, and Rebecca J. Passonneau. 2015 · 2015
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Sentence compression by deletion with lstms
Katja Filippova, Enrique Alfonseca, Carlos Colmenares, Lukasz Kaiser, and Oriol Vinyals. 2015 · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Toward abstractive summarization using semantic representations
Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, and Noah A. Smith. 2015 · 2015
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Masaru Isonuma, Toru Fujino, Junichiro Mori, Yutaka Matsuo, and Ichiro Sakata. 2017 · 2017
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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
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Abstractive document summarization with a graph-based attentional neural model
Jiwei Tan, Xiaojun Wan, and Jianguo Xiao. 2017 · 2017
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Graph-based neural multi-document summarization
Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, and Dragomir Radev. 2017 · 2017
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Efficient summarization with read-again and copy mechanism
Wenyuan Zeng, Wenjie Luo, Sanja Fidler, and Raquel Urtasun. 2017 · 2017
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Selective encoding for abstractive sentence summarization
Qingyu Zhou, Nan Yang, Furu Wei, and Ming Zhou. 2017 · 2017
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Tal Baumel, Matan Eyal, and Michael Elhadad. 2018 · 2018
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Fast abstractive summarization with reinforce-selected sentence rewriting
Yen-Chun Chen and Mohit Bansal. 2018 · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander 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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Improving abstraction in text summarization
Wojciech Kryściński, Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
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Abstract meaning representation for multi-document summarization
Kexin Liao, Logan Lebanoff, and Fei Liu. 2018 · 2018
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Ranking sentences for extractive summarization with reinforcement learning
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
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Structure-infused copy mechanisms for abstractive summarization
Kaiqiang Song, Lin Zhao, and Fei Liu. 2018 · 2018
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Towards a neural network approach to abstractive multi-document summarization
Jianmin Zhang, Jiwei Tan, and Xiaojun Wan. 2018 · 2018
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