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Abstractive text summarization is the task of compressing and rewriting a long document into a short summary while maintaining saliency, directed logical entailment, and non-redundancy.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
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An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani. 1994 · 1994
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Sentence reduction for automatic text summarization
Hongyan Jing. 2000 · 2000
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Summarization beyond sentence extraction: A probabilistic approach to sentence compression
Kevin Knight and Daniel Marcu. 2002 · 2002
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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World wide web site summarization
Yongzheng Zhang, Nur Zincir-Heywood, and Evangelos Milios. 2004 · 2004
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
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Methods for using textual entailment in open-domain question answering
Sanda Harabagiu and Andrew Hickl. 2006 · 2006
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Global inference for sentence compression: An integer linear programming approach
James Clarke and Mirella Lapata. 2008 · 2008
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Automatic summarization from multiple documents
George Giannakopoulos. 2009 · 2009
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Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions
Kavita Ganesan, ChengXiang Zhai, and Jiawei Han. 2010 · 2010
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Abstractive meeting summarization with entailment and fusion
Yashar Mehdad, Giuseppe Carenini, Frank W Tompa, and Raymond T Ng. 2013 · 2013
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Unsupervised sentence enhancement for automatic summarization
Jackie Chi Kit Cheung and Gerald Penn. 2014 · 2014
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Meteor universal: Language specific translation evaluation for any target language
Michael Denkowski and Alon Lavie. 2014 · 2014
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Abstractive summarization of product reviews using discourse structure
Shima Gerani, Yashar Mehdad, Giuseppe Carenini, Raymond T Ng, and Bita Nejat. 2014 · 2014
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Text summarization through entailment-based minimum vertex cover
Anand Gupta, Manpreet Kaur, Adarsh Singh, Aseem Goel, and Shachar Mirkin. 2014 · 2014
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UNAL-NLP: Combining soft cardinality features for semantic textual similarity, relatedness and entailment
Sergio Jimenez, George Duenas, Julia Baquero, Alexander Gelbukh, Av Juan Dios Bátiz, and Av Mendizábal. 2014 · 2014
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Illinois-LH: A denotational and distributional approach to semantics
Alice Lai and Julia Hockenmaier. 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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A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
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Sentence compression by deletion with lstms
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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A decomposable attention model for natural language inference
Ankur P Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Self-critical sequence training for image captioning
Steven J Rennie, Etienne Marcheret, Youssef Mroueh, Jarret Ross, and Vaibhava Goel. 2016 · 2016
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Rnn-based encoder-decoder approach with word frequency estimation
Jun Suzuki and Masaaki Nagata. 2016 · 2016
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Katja Filippova, Enrique Alfonseca, Carlos A 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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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 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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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 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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Reinforcement learning neural turing machines
Wojciech Zaremba and Ilya Sutskever. 2015 · 2015
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Lu Wang, Hema Raghavan, Vittorio Castelli, Radu Florian, and Claire Cardie. 2016 · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
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Text summarization using abstract meaning representation
Shibhansh Dohare and Harish Karnick. 2017 · 2017
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Reinforced video captioning with entailment rewards
Ramakanth Pasunuru and Mohit Bansal. 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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Neural models for key phrase detection and question generation
Sandeep Subramanian, Tong Wang, Xingdi Yuan, and Adam Trischler. 2017 · 2017
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman. 2017 · 2017
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Extractive summarization using multi-task learning with document classification
Masaru Isonuma, Toru Fujino, Junichiro Mori, Yutaka Matsuo, and Ichiro Sakata. 2017 · 2091
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