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Recent pre-trained abstractive summarization systems have started to achieve credible performance, but a major barrier to their use in practice is their propensity to output summaries that are not faithful to the input and that contain factual errors.
Sticking to the facts: Confident decoding for faithful data-to-text generation
Ran Tian, Shashi Narayan, Thibault Sellam, and Ankur P Parikh. 2019 · 1910
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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Information fusion in the context of multi-document summarization
Regina Barzilay, Kathleen R. McKeown, and Michael Elhadad. 1999 · 1999
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Statistics-Based Summarization—Step One: Sentence Compression
Kevin Knight and Daniel Marcu. 2000 · 2000
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Evaluating content selection in summarization: The pyramid method
Ani Nenkova and Rebecca Passonneau. 2004 · 2004
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Guiding semi-supervision with constraint-driven learning
Ming-Wei Chang, Lev Ratinov, and Dan Roth. 2007 · 2007
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Extractive vs. NLG-based abstractive summarization of evaluative text: The effect of corpus controversiality
Giuseppe Carenini and Jackie C. K. Cheung. 2008 · 2008
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Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints
Greg Durrett, Taylor Berg-Kirkpatrick, and Dan Klein. 2016 · 2008
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Non-expert evaluation of summarization systems is risky
Dan Gillick and Yang Liu. 2010 · 2010
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Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation
Yuning Mao, Xiang Ren, Heng Ji, and Jiawei Han. 2020 · 2010
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Jointly Learning to Extract and Compress
Taylor Berg-Kirkpatrick, Dan Gillick, and Dan Klein. 2011 · 2011
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Multiple Aspect Summarization Using Integer Linear Programming
Kristian Woodsend and Mirella Lapata. 2012 · 2012
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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
Cited alongside, same era.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016 · 2016
Cited alongside, same era.
Faithful to the original: Fact aware neural abstractive summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
FEVER: a large-scale dataset for fact extraction and VERification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
Cited alongside, same era.
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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FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization
Esin Durmus, He He, and Mona Diab. 2020 · 2020
Later among the works it cites.
Evaluating factuality in generation with dependency-level entailment
Tanya Goyal and Greg Durrett. 2020a · 2020
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Improved natural language generation via loss truncation
Daniel Kang and Tatsunori Hashimoto. 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
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Fang-Fang Zhang, Jin-ge Yao, and Rui Yan. 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
Cited alongside, same era.
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
Cited alongside, same era.
Analyzing sentence fusion in abstractive summarization
Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, and Fei Liu. 2019 · 2019
Cited alongside, same era.
Combining fact extraction and verification with neural semantic matching networks
Yixin Nie, Haonan Chen, and Mohit Bansal. 2019 · 2019
Cited alongside, same era.
Generating fact checking explanations
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2020 · 2020
Cited alongside, same era.
Factual error correction for abstractive summarization models
Meng Cao, Yue Dong, Jiapeng Wu, and Jackie Chi Kit Cheung. 2020 · 2020
Cited alongside, same era.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Truth or Error? Towards systematic analysis of factual errors in abstractive summaries
Klaus-Michael Lux, Maya Sappelli, and Martha Larson. 2020 · 2020
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan Thomas Mcdonald. 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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Reducing quantity hallucinations in abstractive summarization
Zheng Zhao, Shay B Cohen, and Bonnie Webber. 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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Posterior regularization for structured latent variable models
Kuzman Ganchev, Joao Graça, Jennifer Gillenwater, and Ben Taskar. 2010 · 2049
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