  Similar case retrieval (SCR) is a representative legal AI application that
plays a pivotal role in promoting judicial fairness. However, existing SCR
datasets only focus on the fact description section when judging the similarity
between cases, ignoring other valuable sections (e.g., the court's opinion)
that can provide insightful reasoning process behind. Furthermore, the case
similarities are typically measured solely by the textual semantics of the fact
descriptions, which may fail to capture the full complexity of legal cases from
the perspective of legal knowledge. In this work, we present MUSER, a similar
case retrieval dataset based on multi-view similarity measurement and
comprehensive legal element with sentence-level legal element annotations.
Specifically, we select three perspectives (legal fact, dispute focus, and law
statutory) and build a comprehensive and structured label schema of legal
elements for each of them, to enable accurate and knowledgeable evaluation of
case similarities. The constructed dataset originates from Chinese civil cases
and contains 100 query cases and 4,024 candidate cases. We implement several
text classification algorithms for legal element prediction and various
retrieval methods for retrieving similar cases on MUSER. The experimental
results indicate that incorporating legal elements can benefit the performance
of SCR models, but further efforts are still required to address the remaining
challenges posed by MUSER. The source code and dataset are released at
https://github.com/THUlawtech/MUSER.
