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        <title>Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News</title>
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        <namePart type="given">Ashkan</namePart>
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            <title>Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
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    <abstract>In this paper, we explore the construction of natural language explanations for news claims, with the goal of assisting fact-checking and news evaluation applications. We experiment with two methods: (1) an extractive method based on Biased TextRank – a resource-effective unsupervised graph-based algorithm for content extraction; and (2) an abstractive method based on the GPT-2 language model. We perform comparative evaluations on two misinformation datasets in the political and health news domains, and find that the extractive method shows the most promise.</abstract>
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    <identifier type="doi">10.18653/v1/2021.nlp4if-1.7</identifier>
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%0 Conference Proceedings
%T Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News
%A Kazemi, Ashkan
%A Li, Zehua
%A Pérez-Rosas, Verónica
%A Mihalcea, Rada
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F kazemi-etal-2021-extractive
%X In this paper, we explore the construction of natural language explanations for news claims, with the goal of assisting fact-checking and news evaluation applications. We experiment with two methods: (1) an extractive method based on Biased TextRank – a resource-effective unsupervised graph-based algorithm for content extraction; and (2) an abstractive method based on the GPT-2 language model. We perform comparative evaluations on two misinformation datasets in the political and health news domains, and find that the extractive method shows the most promise.
%R 10.18653/v1/2021.nlp4if-1.7
%U https://aclanthology.org/2021.nlp4if-1.7
%U https://doi.org/10.18653/v1/2021.nlp4if-1.7
%P 45-50

Markdown (Informal)

[Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News](https://aclanthology.org/2021.nlp4if-1.7) (Kazemi et al., NLP4IF 2021)

• Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News (Kazemi et al., NLP4IF 2021)

ACL

• Ashkan Kazemi, Zehua Li, Verónica Pérez-Rosas, and Rada Mihalcea. 2021. Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 45–50, Online. Association for Computational Linguistics.