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We study the problem of domain adaptation for neural abstractive summarization.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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A statistical model for multilingual entity detection and tracking
R Florian, H Hassan, A Ittycheriah, H Jing, N Kambhatla, X Luo, N Nicolov, and S Roukos. 2004 · 2004
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Recognizing contextual polarity in phrase-level sentiment analysis
Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005 · 2005
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Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, Fernando Pereira, et al. 2007 · 2007
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Frustratingly easy domain adaptation
Hal Daume III. 2007 · 2007
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The new york times annotated corpus, 2008
Evan Sandhaus. 2008 · 2008
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Discriminative instance weighting for domain adaptation in statistical machine translation
George Foster, Cyril Goutte, and Roland Kuhn. 2010 · 2010
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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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Domain adaptation to summarize human conversations
Oana Sandu, Giuseppe Carenini, Gabriel Murray, and Raymond Ng. 2010 · 2010
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Automatic summarization
Ani Nenkova, Kathleen McKeown, et al. 2011 · 2011
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Domain-independent abstract generation for focused meeting summarization
Lu Wang and Claire Cardie. 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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Abstractive summarization of product reviews using discourse structure
A neural attention model for abstractive sentence summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
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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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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
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Unsupervised pretraining for sequence to sequence learning
Prajit Ramachandran, Peter J Liu, and Quoc V Le. 2016 · 2016
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Neural headline generation on abstract meaning representation
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Shima Gerani, Yashar Mehdad, Giuseppe Carenini, Raymond T Ng, and Bita Nejat. 2014 · 2014
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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 level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2015 · 2015
Cited alongside, same era.
Sho Takase, Jun Suzuki, Naoaki Okazaki, Tsutomu Hirao, and Masaaki Nagata. 2016 · 2016
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Neural network-based abstract generation for opinions and arguments
Lu Wang and Wang Ling. 2016 · 2016
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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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