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Opinion summarization has been traditionally approached with unsupervised, weakly-supervised and few-shot learning techniques.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
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Unsupervised opinion summarization with noising and denoising
Reinald Kim Amplayo and Mirella Lapata. 2020 · 1945
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
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Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014 · 1992
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Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020 · 2004
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Convex optimization
Stephen Boyd and Lieven Vandenberghe. 2004 · 2004
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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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Variance reduction techniques for gradient estimates in reinforcement learning
Evan Greensmith, Peter L Bartlett, and Jonathan Baxter. 2004 · 2004
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Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
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Estimating mutual information
Alexander Kraskov, Harald Stögbauer, and Peter Grassberger. 2004 · 2004
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Summeval: Re-evaluating summarization evaluation
Alexander R Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2020 · 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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The new york times annotated corpus
Evan Sandhaus. 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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Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman. 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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The neural autoregressive distribution estimator
Hugo Larochelle and Iain Murray. 2011 · 2011
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Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley. 2013 · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
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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
Cited alongside, same era.
Abstractive summarization of product reviews using discourse structure
Shima Gerani, Yashar Mehdad, Giuseppe Carenini, Raymond T. Ng, and Bita Nejat. 2014 · 2014
Cited alongside, same era.
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.
Neural variational inference and learning in belief networks
Andriy Mnih and Karol Gregor. 2014 · 2014
Cited alongside, same era.
Mutual information between discrete and continuous data sets
Controllable abstractive summarization
Angela Fan, David Grangier, and Michael Auli. 2018 · 2018
Later among the works it cites.
Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018 · 2018
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Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies
Max Grusky, Mor Naaman, and Yoav Artzi. 2018 · 2018
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Adapting the neural encoder-decoder framework from single to multi-document summarization
Logan Lebanoff, Kaiqiang Song, and Fei Liu. 2018 · 2018
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
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Meansum: a neural model for unsupervised multi-document abstractive summarization
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Brian C Ross. 2014 · 2014
Cited alongside, same era.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 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.
Abstractive text summarization using sequence-to-sequence RNNs and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Çağlar Gu̇lçehre, and Bing Xiang. 2016 · 2016
Cited alongside, same era.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2017 · 2017
Cited alongside, same era.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Eric Chu and Peter Liu. 2019 · 2019
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Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model
Alexander Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir Radev. 2019 · 2019
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Attention is not Explanation
Sarthak Jain and Byron C. Wallace. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019 · 2019
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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron van den Oord, and Oriol Vinyals. 2019 · 2019
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Red-faced ROUGE: Examining the suitability of ROUGE for opinion summary evaluation
Wenyi Tay, Aditya Joshi, Xiuzhen Zhang, Sarvnaz Karimi, and Stephen Wan. 2019 · 2019
Later among the works it cites.
Extractive opinion summarization in quantized transformer spaces
Stefanos Angelidis, Reinald Kim Amplayo, Yoshihiko Suhara, Xiaolan Wang, and Mirella Lapata. 2020 · 2020
Later among the works it cites.
Few-shot learning for opinion summarization
Arthur Bražinskas, Mirella Lapata, and Ivan Titov. 2020a · 2020
Later among the works it cites.
The summary loop: Learning to write abstractive summaries without examples
Philippe Laban, Andrew Hsi, John Canny, and Marti A. Hearst. 2020 · 2020
Later among the works it cites.
Dice loss for data-imbalanced NLP tasks
Xiaoya Li, Xiaofei Sun, Yuxian Meng, Junjun Liang, Fei Wu, and Jiwei Li. 2020 · 2020
Later among the works it cites.
OpinionDigest: A simple framework for opinion summarization
Yoshihiko Suhara, Xiaolan Wang, Stefanos Angelidis, and Wang-Chiew Tan. 2020 · 2020
Later among the works it cites.
Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2020
Later among the works it cites.
Efficiently summarizing text and graph encodings of multi-document clusters
Ramakanth Pasunuru, Mengwen Liu, Mohit Bansal, Sujith Ravi, and Markus Dreyer. 2021 · 2021
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