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Factual consistency is an essential quality of text summarization models in practical settings.
UNIFIEDQA: Crossing format boundaries with a single QA system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020 · 1907
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
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, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
Earlier work this paper cites.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
Earlier work this paper cites.
Il calcolo delle assicurazioni su gruppi di teste
Carlo E Bonferroni. 1935 · 1935
Earlier work this paper cites.
The Jackknife, the bootstrap and other resampling plans
Bradley Efron. 1982 · 1980
Earlier work this paper cites.
Adversarial nli for factual correctness in text summarisation models
Mario Barrantes, Benedikt Herudek, and Richard Wang. 2020 · 2005
Earlier work this paper cites.
FFCI: A framework for interpretable automatic evaluation of summarization
Fajri Koto, Jey Han Lau, and Timothy Baldwin. 2020 · 2011
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Abstractive text summarization using sequence-to-sequence RNNs and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016 · 2016
Earlier work this paper cites.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Transforming question answering datasets into natural language inference datasets
Dorottya Demszky, Kelvin Guu, and Percy Liang. 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.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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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. 2019 · 2019
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Answers unite! unsupervised metrics for reinforced summarization models
Thomas Scialom, Sylvain Lamprier, Benjamin Piwowarski, and Jacopo Staiano. 2019 · 2019
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Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020
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Fill in the BLANC: Human-free quality estimation of document summaries
Oleg Vasilyev, Vedant Dharnidharka, and John Bohannon. 2020 · 2020
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PEGASUS: pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2020a · 2020
Later among the works it cites.
Bertscore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020b · 2020
Later among the works it cites.
Understanding the extent to which content quality metrics measure the information quality of summaries
Daniel Deutsch and Dan Roth. 2021 · 2021
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Summeval: Re-evaluating summarization evaluation
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Re-evaluating evaluation in text summarization
Manik Bhandari, Pranav Narayan Gour, Atabak Ashfaq, Pengfei Liu, and Graham Neubig. 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.
ELECTRA: pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2020
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Towards question-answering as an automatic metric for evaluating the content quality of a summary
Daniel Deutsch, Tania Bedrax-Weiss, and Dan Roth. 2020 · 2020
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SacreROUGE: An open-source library for using and developing summarization evaluation metrics
Daniel Deutsch and Dan Roth. 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
Cited alongside, same era.
Evaluating factuality in generation with dependency-level entailment
Tanya Goyal and Greg Durrett. 2020 · 2020
Cited alongside, same era.
Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir R. Radev. 2021 · 2021
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Single-dataset experts for multi-dataset qa
Dan Friedman, Ben Dodge, and Danqi Chen. 2021 · 2021
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GO FIGURE: A meta evaluation of factuality in summarization
Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, Yejin Choi, and Jianfeng Gao. 2021 · 2021
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Question answering infused pre-training of general-purpose contextualized representations
Robin Jia, Mike Lewis, and Luke Zettlemoyer. 2021 · 2021
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Ffci: A framework for interpretable automatic evaluation of summarization
Fajri Koto, Timothy Baldwin, and Jey Han Lau. 2021 · 2021
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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. 2021 · 2021
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Mixqg: Neural question generation with mixed answer types
Lidiya Murakhovs’ka, Chien-Sheng Wu, Tong Niu, Wenhao Liu, and Caiming Xiong. 2021 · 2021
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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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Get your vitamin C! robust fact verification with contrastive evidence
Tal Schuster, Adam Fisch, and Regina Barzilay. 2021 · 2021
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Questeval: Summarization asks for fact-based evaluation
Thomas Scialom, Paul-Alexis Dray, Gallinari Patrick, Lamprier Sylvain, Piwowarski Benjamin, Staiano Jacopo, and Wang Alex. 2021 · 2021
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DocNLI: A large-scale dataset for document-level natural language inference
Wenpeng Yin, Dragomir Radev, and Caiming Xiong. 2021 · 2021
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Bartscore: Evaluating generated text as text generation
Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021 · 2021
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