Similar Case Matching (SCM) plays a pivotal role in the legal system by facilitating the efficient identification of similar cases for legal professionals. While previous research has primarily concentrated on enhancing the performance of SCM models, the aspect of interpretability has been neglected. To bridge the gap, this study proposes an integrated pipeline framework for interpretable SCM. The framework comprises four modules: judicial feature sentence identification, case matching, feature sentence alignment, and conflict resolution. In contrast to current SCM methods, our framework first extracts feature sentences within a legal case that contain essential information. Then it conducts case matching based on these extracted features. Subsequently, our framework aligns the corresponding sentences in two legal cases to provide evidence of similarity. In instances where the results of case matching and feature sentence alignment exhibit conflicts, the conflict resolution module resolves these inconsistencies. The experimental results show the effectiveness of our proposed framework, establishing a new benchmark for interpretable SCM.

翻译：相似案例匹配（SCM）在法律系统中发挥着关键作用，能够帮助法律从业者高效识别相似案例。尽管以往研究主要聚焦于提升SCM模型的性能，但其可解释性层面却一直被忽视。为弥补这一空缺，本研究提出了一个面向可解释SCM的集成流水线框架。该框架包含四个模块：司法特征句识别、案例匹配、特征句对齐与冲突消解。与当前SCM方法不同，我们的框架首先从法律案例中提取包含关键信息的特征句，随后基于这些提取的特征进行案例匹配。接着，框架对两个法律案例中的对应句子进行对齐，以提供相似性证据。当案例匹配与特征句对齐结果出现冲突时，冲突消解模块将处理这些不一致性。实验结果表明，所提框架的有效性为可解释SCM建立了新基准。