  Automated fact extraction and verification is a challenging task that
involves finding relevant evidence sentences from a reliable corpus to verify
the truthfulness of a claim. Existing models either (i) concatenate all the
evidence sentences, leading to the inclusion of redundant and noisy
information; or (ii) process each claim-evidence sentence pair separately and
aggregate all of them later, missing the early combination of related sentences
for more accurate claim verification. Unlike the prior works, in this paper, we
propose Hierarchical Evidence Set Modeling (HESM), a framework to extract
evidence sets (each of which may contain multiple evidence sentences), and
verify a claim to be supported, refuted or not enough info, by encoding and
attending the claim and evidence sets at different levels of hierarchy. Our
experimental results show that HESM outperforms 7 state-of-the-art methods for
fact extraction and claim verification. Our source code is available at
https://github.com/ShyamSubramanian/HESM.
