  Justification is an explanation that supports the veracity assigned to a
claim in fact-checking. However, the task of justification generation is
previously oversimplified as summarization of fact-check article authored by
fact-checkers. Therefore, we propose a realistic approach to generate
justification based on retrieved evidence. We present a new benchmark dataset
called ExClaim for \underline{Ex}plainable fact-checking of real-world
\underline{Claim}s, and introduce JustiLM, a novel few-shot
\underline{Justi}fication generation based on retrieval-augmented
\underline{L}anguage \underline{M}odel by using fact-check articles as
auxiliary resource during training only. Experiments show that JustiLM achieves
promising performance in justification generation compared to strong baselines,
and can also enhance veracity classification with a straightforward extension.
