Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News
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.
PDF Abstract NAACL (NLP4IF) 2021 PDF NAACL (NLP4IF) 2021 Abstract
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