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Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News (2104.12918v1)
Published 27 Apr 2021 in cs.CL
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 LLM. 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.