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Neural abstractive summarization models have led to promising results in summarizing relatively short documents.
The structure of a scientific paper
Frederick Suppe. 1998 · 1998
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Using hidden markov modeling to decompose human-written summaries
Hongyan Jing. 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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Using latent semantic analysis in text summarization and summary evaluation
Josef Steinberger and Karel Jezek. 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
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Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013 · 2013
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Generating extractive summaries of scientific paradigms
Vahed Qazvinian, Dragomir R Radev, Saif M Mohammad, Bonnie Dorr, David Zajic, Michael Whidby, and Taesun Moon. 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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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Scientific article summarization using citation-context and article’s discourse structure
Arman Cohan and Nazli Goharian. 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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An improved non-monotonic transition system for dependency parsing
Matthew Honnibal and Mark Johnson. 2015 · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015 · 2015
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A neural attention model for abstractive sentence summarization
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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Coarse-to-fine attention models for document summarization
Jeffrey Ling and Alexander Rush. 2017 · 2017
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Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 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, Christopher Manning, and Peter Liu. 2017 · 2017
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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
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Abstractive sentence summarization with attentive recurrent neural networks
Sumit Chopra, Michael Auli, Alexander M Rush, and SEAS Harvard. 2016 · 2016
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Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O.K. Li. 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, Caglar Gulcehre, Bing Xiang, et al. 2016 · 2016
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Contextualizing citations for scientific summarization using word embeddings and domain knowledge
Arman Cohan and Nazli Goharian. 2017a
Cited in the paper.
Scientific document summarization via citation contextualization and scientific discourse
Arman Cohan and Nazli Goharian. 2017b
Cited in the paper.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Challenges in data-to-document generation
Sam Wiseman, Stuart M Shieber, and Alexander M Rush. 2017 · 2017
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Generating wikipedia by summarizing long sequences
Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. 2018 · 2018
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Cascaded attention based unsupervised information distillation for compressive summarization
Piji Li, Wai Lam, Lidong Bing, Weiwei Guo, and Hang Li. 2017 · 2080
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