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Despite significant progress has been achieved in text summarization, factual inconsistency in generated summaries still severely limits its practical applications.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
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The TIPSTER SUMMAC text summarization evaluation
Inderjeet Mani, David House, Gary Klein, Lynette Hirschman, Therese Firmin, and Beth Sundheim. 1999 · 1999
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Direct and indirect effects
Judea Pearl. 2001 · 2001
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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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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Counterfactual vqa: A cause-effect look at language bias
Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong Wen. 2020 · 2006
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Meteor: An automatic metric for mt evaluation with high levels of correlation with human judgments
Alon Lavie and Abhaya Agarwal. 2007 · 2007
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Discourse constraints for document compression
James Clarke and Mirella Lapata. 2010 · 2010
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Ffci: A framework for interpretable automatic evaluation of summarization
Fajri Koto, Jey Han Lau, and Timothy Baldwin. 2020 · 2011
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Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. 2015 · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Abstractive text summarization using sequence-to-sequence RNNs and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Çaǧlar Gulçehre, and Bing Xiang. 2016 · 2016
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ParlAI: A dialog research software platform
Alexander Miller, Will Feng, Dhruv Batra, Antoine Bordes, Adam Fisch, Jiasen Lu, Devi Parikh, and Jason Weston. 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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A semantic qa-based approach for text summarization evaluation
Ping Chen, Fei Wu, Tong Wang, and Wei Ding. 2018 · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018 · 2018
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Women also snowboard: Overcoming bias in captioning models
Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach. 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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The book of why: the new science of cause and effect
Judea Pearl and Dana Mackenzie. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
Esin Durmus, He He, and Mona Diab. 2020 · 2020
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Controlled hallucinations: Learning to generate faithfully from noisy data
Katja Filippova. 2020 · 2020
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Evaluating the factual consistency of abstractive text summarization
Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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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. 2020 · 2020
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Question answering as an automatic evaluation metric for news article summarization
Matan Eyal, Tal Baumel, and Michael Elhadad. 2019 · 2019
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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Tobias Falke, Leonardo F. R. Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
Assessing the factual accuracy of generated text
Ben Goodrich, Vinay Rao, Peter J. Liu, and Mohammad Saleh. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Cited alongside, same era.
Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
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Understanding neural abstractive summarization models via uncertainty
Jiacheng Xu, Shrey Desai, and Greg Durrett. 2020 · 2020
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SummEval: Re-evaluating Summarization Evaluation
Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021 · 2021
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Looking beyond sentence-level natural language inference for question answering and text summarization
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Lorraine Li, Pavan Kapanipathi, and Kartik Talamadupula. 2021 · 2021
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Questeval: Summarization asks for fact-based evaluation
Thomas Scialom, Paul-Alexis Dray, Patrick Gallinari, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano, and Alex Wang. 2021 · 2021
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On hallucination and predictive uncertainty in conditional language generation
Yijun Xiao and William Yang Wang. 2021 · 2021
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A survey on causal inference
Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang. 2021 · 2021
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