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In this paper, we explore the construction of natural language explanationsfor news claims, with the goal of assisting fact-checking and news evaluationapplications. We experiment with two methods: (1) an extractive method based onBiased TextRank -- a resource-effective unsupervised graph-based algorithm forcontent extraction; and (2) an abstractive method based on the GPT-2 languagemodel. We perform comparative evaluations on two misinformation datasets in thepolitical and health news domains, and find that the extractive method showsthe most promise.

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