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Fighting misinformation is a challenging, yet crucial, task. Despite thegrowing number of experts being involved in manual fact-checking, this activityis time-consuming and cannot keep up with the ever-increasing amount of FakeNews produced daily. Hence, automating this process is necessary to help curbmisinformation. Thus far, researchers have mainly focused on claim veracityclassification. In this paper, instead, we address the generation ofjustifications (textual explanation of why a claim is classified as either trueor false) and benchmark it with novel datasets and advanced baselines. Inparticular, we focus on summarization approaches over unstructured knowledge(i.e. news articles) and we experiment with several extractive and abstractivestrategies. We employed two datasets with different styles and structures, inorder to assess the generalizability of our findings. Results show that injustification production summarization benefits from the claim information,and, in particular, that a claim-driven extractive step improves abstractivesummarization performances. Finally, we show that although cross-datasetexperiments suffer from performance degradation, a unique model trained on acombination of the two datasets is able to retain style information in anefficient manner.

Benchmarking the Generation of Fact Checking Explanations