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

MUSER: A Multi-View Similar Case Retrieval Dataset