  We present SUMO, a neural attention-based approach that learns to establish
the correctness of textual claims based on evidence in the form of text
documents (e.g., news articles or Web documents). SUMO further generates an
extractive summary by presenting a diversified set of sentences from the
documents that explain its decision on the correctness of the textual claim.
Prior approaches to address the problem of fact checking and evidence
extraction have relied on simple concatenation of claim and document word
embeddings as an input to claim driven attention weight computation. This is
done so as to extract salient words and sentences from the documents that help
establish the correctness of the claim. However, this design of claim-driven
attention does not capture the contextual information in documents properly. We
improve on the prior art by using improved claim and title guided hierarchical
attention to model effective contextual cues. We show the efficacy of our
approach on datasets concerning political, healthcare, and environmental
issues.
