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Opinion summarization is the task of automatically creating summaries that reflect subjective information expressed in multiple documents, such as product reviews.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Maxwell Forbes, and Yejin Choi. 2019 · 1904
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Best-worst scaling: A model for the largest difference judgments
Jordan J Louviere and George G Woodworth. 1991 · 1991
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Information fusion in the context of multi-document summarization
Regina Barzilay, Kathleen R McKeown, and Michael Elhadad. 1999 · 1999
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The pagerank citation ranking: Bringing order to the web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999 · 1999
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Lexrank: Graph-based lexical centrality as salience in text summarization
Günes Erkan and Dragomir R Radev. 2004 · 2004
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Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
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Overview of duc 2005
Hoa Trang Dang. 2005 · 2005
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Hierarchical probabilistic neural network language model
Frederic Morin and Yoshua Bengio. 2005 · 2005
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Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
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Moses: Open source toolkit for statistical machine translation
Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, et al. 2007 · 2007
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Topic sentiment mixture: modeling facets and opinions in weblogs
Qiaozhu Mei, Xu Ling, Matthew Wondra, Hang Su, and ChengXiang Zhai. 2007 · 2007
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Extractive vs. nlg-based abstractive summarization of evaluative text: The effect of corpus controversiality
Giuseppe Carenini and Jackie Chi Kit Cheung. 2008 · 2008
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Modeling online reviews with multi-grain topic models
Ivan Titov and Ryan McDonald. 2008 · 2008
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Maxdiff analysis: Simple counting, individual-level logit, and hb
Bryan Orme. 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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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio. 2010 · 2010
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Sentiment analysis and opinion mining
Bing Liu. 2012 · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
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Learning phrase representations using rnn encoder–decoder for statistical machine translation
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
Ruining He and Julian McAuley. 2016 · 2016
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Capturing reliable fine-grained sentiment associations by crowdsourcing and best–worst scaling
Svetlana Kiritchenko and Saif M Mohammad. 2016 · 2016
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Language as a latent variable: Discrete generative models for sentence compression
Yishu Miao and Phil Blunsom. 2016 · 2016
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016 · 2016
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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 · 2017
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Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
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A hybrid approach to multi-document summarization of opinions in reviews
Giuseppe Di Fabbrizio, Amanda Stent, and Robert Gaizauskas. 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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Sentiment analysis algorithms and applications: A survey
Walaa Medhat, Ahmed Hassan, and Hoda Korashy. 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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Skip-thought vectors
Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
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Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
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Using the output embedding to improve language models
Ofir Press and Lior Wolf. 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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Summarizing opinions: Aspect extraction meets sentiment prediction and they are both weakly supervised
Stefanos Angelidis and Mirella Lapata. 2018 · 2018
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Generating wikipedia by summarizing long sequences
Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. 2018 · 2018
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Seqˆ3: Differentiable sequence-to-sequence-to-sequence autoencoder for unsupervised abstractive sentence compression
Christos Baziotis, Ion Androutsopoulos, Ioannis Konstas, and Alexandros Potamianos. 2019 · 2019
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Meansum: a neural model for unsupervised multi-document abstractive summarization
Eric Chu and Peter Liu. 2019 · 2019
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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Tobias Falke, Leonardo FR Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019 · 2019
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Cyclical annealing schedule: A simple approach to mitigating kl vanishing
Hao Fu, Chunyuan Li, Xiaodong Liu, Jianfeng Gao, Asli Celikyilmaz, and Lawrence Carin. 2019 · 2019
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Unsupervised neural single-document summarization of reviews via learning latent discourse structure and its ranking
Masaru Isonuma, Junichiro Mori, and Ichiro Sakata. 2019 · 2019
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