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Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically using rule-based methods.
Neural extractive text summarization with syntactic compression
Jiacheng Xu and Greg Durrett. 2019 · 1902
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Wilson L Taylor. 1953 · 1953
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A trainable document summarizer
Julian Kupiec, Jan Pedersen, and Francine Chen. 1995 · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Text summarization via hidden markov models
John M Conroy and Dianne P O’leary. 2001 · 2001
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Automatic Summarization
Inderjeet Mani. 2001 · 2001
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Mead - a platform for multidocument multilingual text summarization
Dragomir Radev, Timothy Allison, Sasha Blair-Goldensohn, John Blitzer, Arda Çelebi, Stanko Dimitrov, Elliott Drabek, Ali Hakim, Wai Lam, Danyu Liu, Jahna Otterbacher, Hong Qi, Horacio Saggion, Simone Teufel, Michael Topper, Adam Winkel, and Zhu Zhang. 2004 · 2004
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A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization
Ani Nenkova, Lucy Vanderwende, and Kathleen McKeown. 2006 · 2006
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Learning-based single-document summarization with compression and anaphoricity constraints
Greg Durrett, Taylor Berg-Kirkpatrick, and Dan Klein. 2016 · 2008
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Automatic generation of story highlights
Kristian Woodsend and Mirella Lapata. 2010 · 2010
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011 · 2011
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Automatic summarization
Ani Nenkova and Kathleen McKeown. 2011 · 2011
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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The stanford corenlp natural language processing toolkit
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 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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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016 · 2016
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Neural summarization by extracting sentences and words
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
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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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Iterative document representation learning towards summarization with polishing
Xiuying Chen, Shen Gao, Chongyang Tao, Yan Song, Dongyan Zhao, and Rui Yan. 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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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Jianpeng Cheng and Mirella Lapata. 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 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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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 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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Banditsum: Extractive summarization as a contextual bandit
Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, and Jackie Chi Kit Cheung. 2018 · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 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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Ranking sentences for extractive summarization with reinforcement learning
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Linguistically-informed self-attention for semantic role labeling
Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, and Andrew McCallum. 2018 · 2018
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Neural latent extractive document summarization
Xingxing Zhang, Mirella Lapata, Furu Wei, and Ming Zhou. 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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