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Despite tremendous improvements in natural language generation, summarization models still suffer from the unfaithfulness issue.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
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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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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
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The balanced accuracy and its posterior distribution
Kay Henning Brodersen, Cheng Soon Ong, Klaas Enno Stephan, and Joachim M Buhmann. 2010 · 2010
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Çağlar Gulçehre, and Bing Xiang. 2016 · 2016
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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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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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Assessing the factual accuracy of generated text
Ben Goodrich, Vinay Rao, Peter J Liu, and Mohammad Saleh. 2019 · 2019
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Paws: Paraphrase adversaries from word scrambling
Yuan Zhang, Jason Baldridge, and Luheng He. 2019b · 2019
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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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Evaluating factuality in generation with dependency-level entailment
Tanya Goyal and Greg Durrett. 2020 · 2020
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What have we achieved on text summarization?
Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, and Yue Zhang. 2020 · 2020
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Improved natural language generation via loss truncation
Daniel Kang and Tatsunori B Hashimoto. 2020 · 2020
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Evaluating the factual consistency of abstractive text summarization
Wojciech Kryściński, 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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Improving truthfulness of headline generation
Kazuki Matsumaru, Sho Takase, and Naoaki Okazaki. 2020 · 2020
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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2020
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020 · 2020
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Summeval: Re-evaluating summarization evaluation
Alexander Richard Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021 · 2021
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Keep it simple: Unsupervised simplification of multi-paragraph text
Philippe Laban, Tobias Schnabel, Paul Bennett, and Marti A Hearst. 2021 · 2021
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Keyword-aware abstractive summarization by extracting set-level intermediate summaries
Yizhu Liu, Qi Jia, and Kenny Q. Zhu. 2021 · 2021
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Prevent the language model from being overconfident in neural machine translation
Mengqi Miao, Fandong Meng, Yijin Liu, Xiao-Hua Zhou, and Jie Zhou. 2021 · 2021
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Reference-free summarization evaluation via semantic correlation and compression ratio
Yizhu Liu, Qi Jia, and Kenny Q. Zhu. 2022b · 2022
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Online chatgpt: Optimizing language models for dialogue
OpenAI. 2022 · 2022
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Harim+: Evaluating summary quality with hallucination risk: Evaluating summary quality with hallucination risk
Seonil Simon Son, Junsoo Park, Jeong-in Hwang, Junghwa Lee, Hyungjong Noh, and Yeonsoo Lee. 2022 · 2022
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Falsesum: Generating document-level nli examples for recognizing factual inconsistency in summarization
Prasetya Utama, Joshua Bambrick, Nafise Sadat Moosavi, and Iryna Gurevych. 2022 · 2022
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Evaluating factual consistency of summaries with large language models
Shiqi Chen, Siyang Gao, and Junxian He. 2023 · 2023
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Moin Nadeem, Anna Bethke, and Siva Reddy. 2021 · 2021
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Artidoro Pagnoni, Vidhisha Balachandran, and Yulia Tsvetkov. 2021 · 2021
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Questeval: Summarization asks for fact-based evaluation
Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano, Alex Wang, and Patrick Gallinari. 2021 · 2021
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Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, and Bolin Ding. 2021 · 2021
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