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Despite recent progress in abstractive summarization, models often generate summaries with factual errors.
ROUGE: A Package for Automatic Evaluation of Summaries
Chin-Yew Lin. 2004 · 2004
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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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A Neural Attention Model for Sentence Summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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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
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.
NewsQA: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017 · 2017
Earlier work this paper cites.
Faithful to the Original: Fact-Aware Neural Abstractive Summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018 · 2018
Earlier work this paper cites.
A Semantic QA-Based Approach for Text Summarization Evaluation
Ping Chen, Fei Wu, Tong Wang, and Wei Ding. 2018 · 2018
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Transforming Question Answering Datasets Into Natural Language Inference Datasets
Dorottya Demszky, Kelvin Guu, and Percy Liang. 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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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Earlier work this paper cites.
Question answering as an automatic evaluation metric for news article summarization
Matan Eyal, Tal Baumel, and Michael Elhadad. 2019 · 2019
Earlier work this paper cites.
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
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Neural text summarization: A critical evaluation
Wojciech Kryscinski, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
Earlier work this paper cites.
Answers unite! unsupervised metrics for reinforced summarization models
Thomas Scialom, Sylvain Lamprier, Benjamin Piwowarski, and Jacopo Staiano. 2019 · 2019
Cited alongside, same era.
BERTScore: Evaluating Text Generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 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.
MOCHA: A dataset for training and evaluating generative reading comprehension metrics
Anthony Chen, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2020 · 2020
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
Cited alongside, same era.
FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
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
Later among the works it cites.
Question-Based Salient Span Selection for More Controllable Text Summarization
Daniel Deutsch and Dan Roth. 2021 · 2021
Later among the works it cites.
SummEval: Re-evaluating summarization evaluation
Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021 · 2021
Later among the works it cites.
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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.
Improved natural language generation via loss truncation
Daniel Kang and Tatsunori B. Hashimoto. 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, Ves Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
On Faithfulness and Factuality in Abstractive Summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan Mcdonald. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Annotating and modeling fine-grained factuality in summarization
Tanya Goyal and Greg Durrett. 2021 · 2021
Later among the works it cites.
Using question answering rewards to improve abstractive summarization
Chulaka Gunasekara, Guy Feigenblat, Benjamin Sznajder, Ranit Aharonov, and Sachindra Joshi. 2021 · 2021
Later among the works it cites.
Entity-level factual consistency of abstractive text summarization
Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, and Bing Xiang. 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
Later among the works it cites.
QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions
Daniela Brook Weiss, Paul Roit, Ayal Klein, Ori Ernst, and Ido Dagan. 2021 · 2021
Later among the works it cites.
QAFactEval: Improved QA-based factual consistency evaluation for summarization
Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, and Caiming Xiong. 2022 · 2022
Closest in time.
SummaC: Re-visiting NLI-based models for inconsistency detection in summarization
Philippe Laban, Tobias Schnabel, Paul N. Bennett, and Marti A. Hearst. 2022 · 2022
Closest in time.
Factgraph: Evaluating factuality in summarization with semantic graph representations
Leonardo F. R. Ribeiro, Mengwen Liu, Iryna Gurevych, Markus Dreyer, and Mohit Bansal. 2022 · 2022
Closest in time.