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Most of the current abstractive text summarization models are based on the sequence-to-sequence model (Seq2Seq).
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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LCSTS: A large scale chinese short text summarization dataset
Baotian Hu, Qingcai Chen, and Fangze Zhu. 2015 · 1972
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Automatic evaluation of summaries using n-gram co-occurrence statistics
Chin-Yew Lin and Eduard H. Hovy. 2003 · 2003
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MEAD - A platform for multidocument multilingual text summarization
Dragomir R. Radev, Timothy Allison, Sasha Blair-Goldensohn, John Blitzer, Arda Çelebi, Stanko Dimitrov, Elliott Drábek, Ali Hakim, Wai Lam, Danyu Liu, Jahna Otterbacher, Hong Qi, Horacio Saggion, Simone Teufel, Michael Topper, Adam Winkel, and Zhu Zhang. 2004 · 2004
Earlier work this paper cites.
Modeling word perception using the elman network
Cheng-Yuan Liou, Jau-Chi Huang, and Wen-Chie Yang. 2008 · 2008
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Learning deep architectures for AI
Yoshua Bengio. 2009 · 2009
Earlier work this paper cites.
Automatic generation of story highlights
Kristian Woodsend and Mirella Lapata. 2010 · 2010
Earlier work this paper cites.
How noisy social media text, how diffrnt social media sources?
Timothy Baldwin, Paul Cook, Marco Lui, Andrew MacKinlay, and Li Wang. 2013 · 2013
Earlier work this paper cites.
From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews
Julian John McAuley and Jure Leskovec. 2013 · 2013
Earlier work this paper cites.
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013 · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Autoencoder for words
Cheng-Yuan Liou, Wei-Chen Cheng, Jiun-Wei Liou, and Daw-Ran Liou. 2014 · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
Cited alongside, same era.
Distraction-based neural networks for modeling documents
Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, and Hui Jiang. 2016 · 2015
Cited alongside, same era.
On using very large target vocabulary for neural machine translation
Abstractive sentence summarization with attentive recurrent neural networks
Sumit Chopra, Michael Auli, and Alexander M. Rush. 2016 · 2016
Later among the works it cites.
Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O. K. Li. 2016 · 2016
Later among the works it cites.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cícero Nogueira dos Santos, Çaglar Gülçehre, and Bing Xiang. 2016 · 2016
Later among the works it cites.
Neural headline generation on abstract meaning representation
Sho Takase, Jun Suzuki, Naoaki Okazaki, Tsutomu Hirao, and Masaaki Nagata. 2016 · 2016
Later among the works it cites.
Improving semantic relevance for sequence-to-sequence learning of chinese social media text summarization
Shuming Ma, Xu Sun, Jingjing Xu, Houfeng Wang, Wenjie Li, and Qi Su. 2017 · 2017
Later among the works it cites.
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Sébastien Jean, KyungHyun Cho, Roland Memisevic, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
A hierarchical neural autoencoder for paragraphs and documents
Jiwei Li, Minh-Thang Luong, and Dan Jurafsky. 2015 · 2015
Cited alongside, same era.
Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
A neural attention model for abstractive sentence summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Cited alongside, same era.
Neural summarization by extracting sentences and words
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
Cited alongside, same era.
Label embedding network: Learning label representation for soft training of deep networks
Xu Sun, Bingzhen Wei, Xuancheng Ren, and Shuming Ma. 2017b
Cited in the paper.
Jingjing Xu, Xu Sun, Xuancheng Ren, Junyang Lin, Binzhen Wei, and Wei Li. 2018a
Cited in the paper.
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Later among the works it cites.
meprop: Sparsified back propagation for accelerated deep learning with reduced overfitting
Xu Sun, Xuancheng Ren, Shuming Ma, and Houfeng Wang. 2017a · 2017
Later among the works it cites.
Query and output: Generating words by querying distributed word representations for paraphrase generation
Shuming Ma, Xu Sun, Wei Li, Sujian Li, Wenjie Li, and Xuancheng Ren. 2018 · 2018
Closest in time.
Unpaired sentiment-to-sentiment translation: A cycled reinforcement learning approach
Jingjing Xu, Xu Sun, Qi Zeng, Xiaodong Zhang, Xuancheng Ren, Houfeng Wang, and Wenjie Li. 2018b · 2018
Closest in time.
Deep recurrent generative decoder for abstractive text summarization
Piji Li, Wai Lam, Lidong Bing, and Zihao Wang. 2017 · 2091
Closest in time.