  Fact-checking is the task of verifying the factuality of a given claim by
examining the available evidence. High-quality evidence plays a vital role in
enhancing fact-checking systems and facilitating the generation of explanations
that are understandable to humans. However, the provision of both sufficient
and relevant evidence for explainable fact-checking systems poses a challenge.
To tackle this challenge, we propose a method based on a Large Language Model
to automatically retrieve and summarize evidence from the Web. Furthermore, we
construct RU22Fact, a novel multilingual explainable fact-checking dataset on
the Russia-Ukraine conflict in 2022 of 16K samples, each containing real-world
claims, optimized evidence, and referenced explanation. To establish a baseline
for our dataset, we also develop an end-to-end explainable fact-checking system
to verify claims and generate explanations. Experimental results demonstrate
the prospect of optimized evidence in increasing fact-checking performance and
also indicate the possibility of further progress in the end-to-end claim
verification and explanation generation tasks.
