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An accurate abstractive summary of a document should contain all its salient information and should be logically entailed by the input document.
Computer-intensive methods for testing hypotheses
Eric W Noreen. 1989 · 1989
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An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani. 1994 · 1994
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Multitask learning
Rich Caruana. 1998 · 1998
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Cut and paste based text summarization
Hongyan Jing and Kathleen R. McKeown. 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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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
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Multi-task feature learning
Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil. 2007 · 2007
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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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Learning task grouping and overlap in multi-task learning
Abhishek Kumar and Hal Daumé III. 2012 · 2012
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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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A sentence compression based framework to query-focused multi-document summarization
Lu Wang, Hema Raghavan, Vittorio Castelli, Radu Florian, and Claire Cardie. 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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The Stanford CoreNLP natural language processing toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 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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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero Nogueira dos santos, Caglar Gulcehre, and Bing Xiang. 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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Rnn-based encoder-decoder approach with word frequency estimation
Jun Suzuki and Masaaki Nagata. 2016 · 2016
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus. 2014 · 2016
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What do neural machine translation models learn about morphology?
Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and James Glass. 2017 · 2017
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Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick. 2015 · 2015
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Sentence compression by deletion with lstms
Katja Filippova, Enrique Alfonseca, Carlos A Colmenares, Lukasz Kaiser, and Oriol Vinyals. 2015 · 2015
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A reinforcement learning approach for adaptive single-and multi-document summarization
Stefan Henß, Margot Mieskes, and Iryna Gurevych. 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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Predicting salient updates for disaster summarization
Chris Kedzie, Kathleen McKeown, and Fernando Diaz. 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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Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017 · 2017
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Text summarization using abstract meaning representation
Shibhansh Dohare and Harish Karnick. 2017 · 2017
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Learning to ask: Neural question generation for reading comprehension
Xinya Du, Junru Shao, and Claire Cardie. 2017 · 2017
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Bringing structure into summaries: Crowdsourcing a benchmark corpus of concept maps
Tobias Falke and Iryna Gurevych. 2017 · 2017
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A joint many-task model: Growing a neural network for multiple nlp tasks
Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, and Richard Socher. 2017 · 2017
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Lukasz Kaiser, Aidan N. Gomez, Noam Shazeer, Ashish Vaswani, Niki Parmar, Llion Jones, and Jakob Uszkoreit. 2017 · 2017
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Multi-task video captioning with video and entailment generation
Ramakanth Pasunuru and Mohit Bansal. 2017 · 2017
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Towards improving abstractive summarization via entailment generation
Ramakanth Pasunuru, Han Guo, and Mohit Bansal. 2017 · 2017
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Sluice networks: Learning what to share between loosely related tasks
Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Sogaard. 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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Abstractive document summarization with a graph-based attentional neural model
Jiwei Tan, Xiaojun Wan, and Jianguo Xiao. 2017 · 2017
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Multi-reward reinforced summarization with saliency and entailment
Ramakanth Pasunuru and Mohit Bansal. 2018 · 2018
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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
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