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arXiv · 2104.12918

Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News

Ashkan Kazemi·Zehua Li·Verónica Pérez-Rosas·Rada Mihalcea
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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.

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Ashkan Kazemi, Zehua Li, Verónica Pérez-Rosas, Rada Mihalcea. 2021-04-27. Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News. https://arxiv.org/abs/2104.12918

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