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Abstractive summarization systems aim to produce more coherent and concise summaries than their extractive counterparts.
DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion
Mor Geva, Eric Malmi, Idan Szpektor, and Jonathan Berant. 2019 · 1902
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Topical coherence for graph-based extractive summarization
Daraksha Parveen, Hans-Martin Ramsl, and Michael Strube. 2015 · 1954
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The automatic creation of literature abstracts
Hans Peter Luhn. 1958 · 1958
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Approximate randomization tests
Eugene S Edgington. 1969 · 1969
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Gradient-based learning algorithms for recurrent
Ronald J Williams and David Zipser. 1995 · 1995
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Automatic summarizing: factors and directions
K Sparck Jones et al. 1999 · 1999
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Integrating cohesion and coherence for automatic summarization
Laura Alonso i Alemany and Maria Fuentes Fort. 2003 · 2003
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Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics
Chin-Yew Lin and Eduard Hovy. 2003 · 2003
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Catching the drift: Probabilistic content models, with applications to generation and summarization
Regina Barzilay and Lillian Lee. 2004 · 2004
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Automatic evaluation of text coherence: Models and representations
Mirella Lapata and Regina Barzilay. 2005 · 2005
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Back to basics: Classy 2006
John M Conroy, Judith D Schlesinger, Dianne P O’leary, and Jade Goldstein. 2006 · 2006
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Extractive summarization using inter- and intra- event relevance
Wenjie Li, Mingli Wu, Qin Lu, Wei Xu, and Chunfa Yuan. 2006 · 2006
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Modeling local coherence: An entity-based approach
Regina Barzilay and Mirella Lapata. 2008 · 2008
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Entity-driven rewrite for multi-document summarization
Ani Nenkova. 2008 · 2008
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The new york times annotated corpus
Evan Sandhaus. 2008 · 2008
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Information status distinctions and referring expressions: An empirical study of references to people in news summaries
Advaith Siddharthan, Ani Nenkova, and Kathleen McKeown. 2011 · 2011
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Towards coherent multi-document summarization
Janara Christensen, Stephen Soderland, Oren Etzioni, et al. 2013 · 2013
Cited alongside, same era.
Graph-based local coherence modeling
Camille Guinaudeau and Michael Strube. 2013 · 2013
Cited alongside, same era.
A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization
Lu Wang, Hema Raghavan, Vittorio Castelli, Radu Florian, and Claire Cardie. 2013 · 2013
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
Cited alongside, same era.
The Stanford CoreNLP natural language processing toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014 · 2014
Challenges in data-to-document generation
Sam Wiseman, Stuart Shieber, and Alexander Rush. 2017 · 2017
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Entity commonsense representation for neural abstractive summarization
Reinald Kim Amplayo, Seonjae Lim, and Seung-won Hwang. 2018 · 2018
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Faithful to the original: Fact aware neural abstractive summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018 · 2018
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Deep communicating agents for abstractive summarization
Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi. 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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Robust neural abstractive summarization systems and evaluation against adversarial information
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Cited alongside, same era.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
Reader-aware multi-document summarization via sparse coding
Piji Li, Lidong Bing, Wai Lam, Hang Li, and Yi Liao. 2015 · 2015
Cited alongside, same era.
Chia-Wei Liu, Ryan Lowe, Iulian V Serban, Michael Noseworthy, Laurent Charlin, and Joelle Pineau. 2016 · 2016
Cited alongside, same era.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre, Bing Xiang, et al. 2016 · 2016
Cited alongside, same era.
Generating coherent summaries of scientific articles using coherence patterns
Daraksha Parveen, Mohsen Mesgar, and Michael Strube. 2016 · 2016
Cited alongside, same era.
Lisa Fan, Dong Yu, and Lu Wang. 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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A unified model for extractive and abstractive summarization using inconsistency loss
Wan-Ting Hsu, Chieh-Kai Lin, Ming-Ying Lee, Kerui Min, Jing Tang, and Min Sun. 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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Guiding generation for abstractive text summarization based on key information guide network
Chenliang Li, Weiran Xu, Si Li, and Sheng Gao. 2018 · 2018
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A neural local coherence model for text quality assessment
Mohsen Mesgar and Michael Strube. 2018 · 2018
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Learning to extract coherent summary via deep reinforcement learning
Yuxiang Wu and Baotian Hu. 2018 · 2018
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Neural document summarization by jointly learning to score and select sentences
Qingyu Zhou, Nan Yang, Furu Wei, Shaohan Huang, Ming Zhou, and Tiejun Zhao. 2018 · 2018
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A model of coherence based on distributed sentence representation
Jiwei Li and Eduard Hovy. 2014 · 2048
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