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Sequence-to-sequence (seq2seq) neural models have been actively investigated for abstractive summarization.
Binary codes capable of correcting deletions, insertions and reversals
VI Levenshtein. 1966 · 1966
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The berkeley framenet project
Collin F Baker, Charles J Fillmore, and John B Lowe. 1998 · 1998
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Automatic summarizing: factors and directions
K Sparck Jones et al. 1999 · 1999
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From treebank to propbank
Paul Kingsbury and Martha Palmer. 2002 · 2002
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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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Textrank: Bringing order into text
Rada Mihalcea and Paul Tarau. 2004 · 2004
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Framewise phoneme classification with bidirectional lstm and other neural network architectures
Alex Graves and Jürgen Schmidhuber. 2005 · 2005
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The new york times annotated corpus
Evan Sandhaus. 2008 · 2008
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer. 2011 · 2011
Earlier work this paper cites.
Automatic summarization
Ani Nenkova, Kathleen McKeown, et al. 2011 · 2011
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Abstract meaning representation for sembanking
Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 2013 · 2013
Earlier work this paper cites.
Domain-independent abstract generation for focused meeting summarization
Lu Wang and Claire Cardie. 2013 · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Meteor universal: Language specific translation evaluation for any target language
Michael Denkowski and Alon Lavie. 2014 · 2014
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The Stanford CoreNLP natural language processing toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014 · 2014
Earlier work this paper cites.
Modelling events through memory-based, open-ie patterns for abstractive summarization
Daniele Pighin, Marco Cornolti, Enrique Alfonseca, and Katja Filippova. 2014 · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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.
Multi-task sequence to sequence learning
Minh-Thang Luong, Quoc V Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser. 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.
A hierarchical recurrent encoder-decoder for generative context-aware query suggestion
Deep semantic role labeling: What works and what’s next
Luheng He, Kenton Lee, Mike Lewis, and Luke Zettlemoyer. 2017 · 2017
Later among the works it cites.
Extractive summarization using multi-task learning with document classification
Masaru Isonuma, Toru Fujino, Junichiro Mori, Yutaka Matsuo, and Ichiro Sakata. 2017 · 2017
Later among the works it cites.
Dual attention networks for multimodal reasoning and matching
Hyeonseob Nam, Jung-Woo Ha, and Jeonghee Kim. 2017 · 2017
Later among the works it cites.
Towards improving abstractive summarization via entailment generation
Ramakanth Pasunuru, Han Guo, and Mohit Bansal. 2017 · 2017
Later among the works it cites.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
Later among the works it cites.
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Alessandro Sordoni, Yoshua Bengio, Hossein Vahabi, Christina Lioma, Jakob Grue Simonsen, and Jian-Yun Nie. 2015 · 2015
Cited alongside, same era.
Grammar as a foreign language
Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey Hinton. 2015 · 2015
Cited alongside, same era.
Stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking
Tsung-Hsien Wen, Milica Gasic, Dongho Kim, Nikola Mrksic, Pei-Hao Su, David Vandyke, and Steve Young. 2015 · 2015
Cited alongside, same era.
Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor OK Li. 2016 · 2016
Cited alongside, same era.
A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016 · 2016
Cited alongside, same era.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Ça glar Gulçehre, and Bing Xiang. 2016 · 2016
Cited alongside, same era.
Temporal attention model for neural machine translation
Baskaran Sankaran, Haitao Mi, Yaser Al-Onaizan, and Abe Ittycheriah. 2016 · 2016
Cited alongside, same era.
Hao Peng, Sam Thomson, and Noah A Smith. 2017 · 2017
Later among the works it cites.
Semi-supervised multitask learning for sequence labeling
Marek Rei. 2017 · 2017
Later among the works it cites.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
Later among the works it cites.
Generating high-quality and informative conversation responses with sequence-to-sequence models
Yuanlong Shao, Stephan Gouws, Denny Britz, Anna Goldie, Brian Strope, and Ray Kurzweil. 2017 · 2017
Later among the works it cites.
Abstractive document summarization with a graph-based attentional neural model
Jiwei Tan, Xiaojun Wan, and Jianguo Xiao. 2017 · 2017
Later among the works it cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Later among the works it cites.
Faithful to the original: Fact aware neural abstractive summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018 · 2018
Closest in time.
Deep communicating agents for abstractive summarization
Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi. 2018 · 2018
Closest in time.
Fast abstractive summarization with reinforce-selected sentence rewriting
Yen-Chun Chen and Mohit Bansal. 2018 · 2018
Closest in time.
Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies
Max Grusky, Mor Naaman, and Yoav Artzi. 2018 · 2018
Closest in time.