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Despite significant progress in understanding and improving faithfulness in abstractive summarization, the question of how decoding strategies affect faithfulness is less studied.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
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Content Analysis: An Introduction to Its Methodology
K. Krippendorff. 1980 · 1980
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Model compression
Cristian Buciluundefined, Rich Caruana, and Alexandru Niculescu-Mizil. 2006 · 2006
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Teaching machines to read and comprehend
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Sequence-level knowledge distillation
Yoon Kim and Alexander M. Rush. 2016 · 2016
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Best-worst scaling more reliable than rating scales: A case study on sentiment intensity annotation
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Wikihow: A large scale text summarization dataset
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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
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Multi-fact correction in abstractive text summarization
Yue Dong, Shuohang Wang, Zhe Gan, Yu Cheng, Jackie Chi Kit Cheung, and Jingjing Liu. 2020 · 2020
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
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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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If beam search is the answer, what was the question?
Clara Meister, Ryan Cotterell, and Tim Vieira. 2020a · 2020
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Pre-trained summarization distillation
Sam Shleifer and Alexander M. Rush. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Understanding factuality in abstractive summarization with FRANK: A benchmark for factuality metrics
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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Faithful or extractive? on mitigating the faithfulness-abstractiveness trade-off in abstractive summarization
Faisal Ladhak, Esin Durmus, He He, Claire Cardie, and Kathleen McKeown. 2022 · 2022
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NeuroLogic a*esque decoding: Constrained text generation with lookahead heuristics
Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, and Yejin Choi. 2022 · 2022
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A well-composed text is half done! composition sampling for diverse conditional generation
Shashi Narayan, Gonçalo Simões, Yao Zhao, Joshua Maynez, Dipanjan Das, Michael Collins, and Mirella Lapata. 2022 · 2022
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Towards summary candidates fusion
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Mutual information alleviates hallucinations in abstractive summarization
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FactPEGASUS: Factuality-aware pre-training and fine-tuning for abstractive summarization
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Entity-based spancopy for abstractive summarization to improve the factual consistency
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Massive-scale decoding for text generation using lattices
Jiacheng Xu, Siddhartha Jonnalagadda, and Greg Durrett. 2022 · 2022
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Improving the faithfulness of abstractive summarization via entity coverage control
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Evaluating the tradeoff between abstractiveness and factuality in abstractive summarization
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