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The propensity of abstractive summarization models to make factual errors has been studied extensively, including design of metrics to detect factual errors and annotation of errors in current systems' outputs.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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TextRank: Bringing order into text
Rada Mihalcea and Paul Tarau. 2004 · 2004
Earlier work this paper cites.
Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomás Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017 · 2017
Earlier work this paper cites.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Fast abstractive summarization with reinforce-selected sentence rewriting
Yen-Chun Chen and Mohit Bansal. 2018 · 2018
Earlier work this paper cites.
BanditSum: Extractive summarization as a contextual bandit
Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, and Jackie Chi Kit Cheung. 2018 · 2018
Earlier work this paper cites.
Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018 · 2018
Earlier work this paper cites.
Soft layer-specific multi-task summarization with entailment and question generation
Han Guo, Ramakanth Pasunuru, and Mohit Bansal. 2018 · 2018
Earlier work this paper cites.
A unified model for extractive and abstractive summarization using inconsistency loss
Wan-Ting Hsu, Chieh-Kai Lin, Ming-Ying Lee, Kerui Min, Jing Tang, and Min Sun. 2018 · 2018
Earlier work this paper cites.
Closed-book training to improve summarization encoder memory
Yichen Jiang and Mohit Bansal. 2018 · 2018
Earlier work this paper cites.
Improving abstraction in text summarization
Wojciech Kryściński, Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Multi-reward reinforced summarization with saliency and entailment
Ramakanth Pasunuru and Mohit Bansal. 2018 · 2018
Earlier work this paper cites.
Learning to extract coherent summary via deep reinforcement learning
Yuxiang Wu and Baotian Hu. 2018 · 2018
Cited alongside, same era.
Neural document summarization by jointly learning to score and select sentences
Qingyu Zhou, Nan Yang, Furu Wei, Shaohan Huang, Ming Zhou, and Tiejun Zhao. 2018 · 2018
Cited alongside, same era.
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 F. R. Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
Cited alongside, same era.
Optimizing the factual correctness of a summary: A study of summarizing radiology reports
Yuhao Zhang, Derek Merck, Emily Tsai, Christopher D. Manning, and Curtis Langlotz. 2020 · 2020
Later among the works it cites.
Reducing quantity hallucinations in abstractive summarization
Zheng Zhao, Shay B. Cohen, and Bonnie Webber. 2020 · 2020
Later among the works it cites.
CLIFF: Contrastive learning for improving faithfulness and factuality in abstractive summarization
Shuyang Cao and Lu Wang. 2021 · 2021
Later among the works it cites.
Improving faithfulness in abstractive summarization with contrast candidate generation and selection
Sihao Chen, Fan Zhang, Kazoo Sone, and Dan Roth. 2021 · 2021
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Annotating and modeling fine-grained factuality in summarization
Tanya Goyal and Greg Durrett. 2021 · 2021
Later among the works it cites.
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Language Models are Unsupervised Multitask Learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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.
FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
Esin Durmus, He He, and Mona Diab. 2020 · 2020
Cited alongside, same era.
Evaluating factuality in generation with dependency-level entailment
Tanya Goyal and Greg Durrett. 2020 · 2020
Cited alongside, same era.
What have we achieved on text summarization?
Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, and Yue Zhang. 2020 · 2020
Cited alongside, same era.
Evaluating the factual consistency of abstractive text summarization
Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Understanding factuality in abstractive summarization with FRANK: A benchmark for factuality metrics
Artidoro Pagnoni, Vidhisha Balachandran, and Yulia Tsvetkov. 2021 · 2021
Later among the works it cites.
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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Gradient-based adversarial factual consistency evaluation for abstractive summarization
Zhiyuan Zeng, Jiaze Chen, Weiran Xu, and Lei Li. 2021 · 2021
Later among the works it cites.
EmailSum: Abstractive email thread summarization
Shiyue Zhang, Asli Celikyilmaz, Jianfeng Gao, and Mohit Bansal. 2021 · 2021
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Hallucinated but factual! inspecting the factuality of hallucinations in abstractive summarization
Meng Cao, Yue Dong, and Jackie Cheung. 2022 · 2022
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News summarization and evaluation in the era of gpt-3
Tanya Goyal, Junyi Jessy Li, and Greg Durrett. 2022 · 2022
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SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization
Philippe Laban, Tobias Schnabel, Paul N. Bennett, and Marti A. Hearst. 2022 · 2022
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CONFIT: Toward faithful dialogue summarization with linguistically-informed contrastive fine-tuning
Xiangru Tang, Arjun Nair, Borui Wang, Bingyao Wang, Jai Desai, Aaron Wade, Haoran Li, Asli Celikyilmaz, Yashar Mehdad, and Dragomir Radev. 2022 · 2022
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Chatgpt as a factual inconsistency evaluator for abstractive text summarization
Zheheng Luo, Qianqian Xie, and Sophia Ananiadou. 2023 · 2023
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Evaluating large language models on medical evidence summarization
Liyan Tang, Zhaoyi Sun, Betina Idnay, Jordan G Nestor, Ali Soroush, Pierre A. Elias, Ziyang Xu, Ying Ding, Greg Durrett, Justin Rousseau, Chunhua Weng, and Yifan Peng. 2023 · 2023
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Is chatgpt a good nlg evaluator? a preliminary study
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