  Claim verification in real-world settings (e.g. against a large collection of
candidate evidences retrieved from the web) typically requires identifying and
aggregating a complete set of evidence pieces that collectively provide full
support to the claim. The problem becomes particularly challenging when there
exists distinct sets of evidence that could be used to verify the claim from
different perspectives. In this paper, we formally define and study the problem
of identifying such minimal evidence groups (MEGs) for claim verification. We
show that MEG identification can be reduced from Set Cover problem, based on
entailment inference of whether a given evidence group provides full/partial
support to a claim. Our proposed approach achieves 18.4% and 34.8% absolute
improvements on the WiCE and SciFact datasets over LLM prompting. Finally, we
demonstrate the benefits of MEGs in downstream applications such as claim
generation.
