Similar Case Matching (SCM) is designed to determine whether two cases are
similar. The task has an essential role in the legal system, helping legal
professionals to find relevant cases quickly and thus deal with them more
efficiently. Existing research has focused on improving the model's performance
but not on its interpretability. Therefore, this paper proposes a pipeline
framework for interpretable SCM, which consists of four modules: a judicial
feature sentence identification module, a case matching module, a feature
sentence alignment module, and a conflict disambiguation module. Unlike
existing SCM methods, our framework will identify feature sentences in a case
that contain essential information, perform similar case matching based on the
extracted feature sentence results, and align the feature sentences in the two
cases to provide evidence for the similarity of the cases. SCM results may
conflict with feature sentence alignment results, and our framework further
disambiguates against this inconsistency. The experimental results show the
effectiveness of our framework, and our work provides a new benchmark for
interpretable SCM.

本文提出了一个管道框架来实现可解释的 SCM，该框架标识了包含基本信息的案例特征句子，根据提取的特征句子结果执行类似案例匹配，并对两种情况的特征句子进行对齐以提供用于相似性证据的特征句子。