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Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents, such as user reviews of a product.
Efficient adaptation of pretrained transformers for abstractive summarization
Andrew Hoang, Antoine Bosselut, Asli Celikyilmaz, and Yejin Choi. 2019 · 1906
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019 · 1910
Earlier work this paper cites.
Statistical analysis of non-lattice data
Julian Besag. 1975 · 1975
Earlier work this paper cites.
A learning algorithm for continually running fully recurrent neural networks
Ronald J Williams and David Zipser. 1989 · 1989
Earlier work this paper cites.
Best-worst scaling: A model for the largest difference judgments
Jordan J Louviere and George G Woodworth. 1991 · 1991
Earlier work this paper cites.
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
Earlier work this paper cites.
Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
Earlier work this paper cites.
Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman. 2009 · 2009
Earlier work this paper cites.
Maxdiff analysis: Simple counting, individual-level logit, and hb
Bryan Orme. 2009 · 2009
Earlier work this paper cites.
Opinosis: A graph based approach to abstractive summarization of highly redundant opinions
Kavita Ganesan, ChengXiang Zhai, and Jiawei Han. 2010 · 2010
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio. 2010 · 2010
Earlier work this paper cites.
A hybrid approach to multi-document summarization of opinions in reviews
Giuseppe Di Fabbrizio, Amanda Stent, and Robert Gaizauskas. 2014 · 2014
Earlier work this paper cites.
Abstractive summarization of product reviews using discourse structure
Shima Gerani, Yashar Mehdad, Giuseppe Carenini, Raymond T Ng, and Bita Nejat. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Sentiment analysis algorithms and applications: A survey
Walaa Medhat, Ahmed Hassan, and Hoda Korashy. 2014 · 2014
Cited alongside, same era.
Best-worst scaling: Theory, methods and applications
Jordan J Louviere, Terry N Flynn, and Anthony Alfred John Marley. 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.
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
Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton. 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Later among the works it cites.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola. 2017 · 2017
Later among the works it cites.
Summarizing opinions: Aspect extraction meets sentiment prediction and they are both weakly supervised
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Svetlana Kiritchenko and Saif M Mohammad. 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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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. 2016 · 2016
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Controlling linguistic style aspects in neural language generation
Jessica Ficler and Yoav Goldberg. 2017 · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 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 · 2017
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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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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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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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Red-faced rouge: Examining the suitability of rouge for opinion summary evaluation
Wenyi Tay, Aditya Joshi, Xiuzhen Jenny Zhang, Sarvnaz Karimi, and Stephen Wan. 2019 · 2019
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Unsupervised opinion summarization with noising and denoising
Reinald Kim Amplayo and Mirella Lapata. 2020 · 2020
Closest in time.
Unsupervised opinion summarization as copycat-review generation
Arthur Bražinskas, Mirella Lapata, and Ivan Titov. 2020 · 2020
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Opiniondigest: A simple framework for opinion summarization
Yoshihiko Suhara, Xiaolan Wang, Stefanos Angelidis, and Wang-Chiew Tan. 2020 · 2020
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