  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.
