Though many algorithms can be used to automatically summarize legal case decisions, most fail to incorporate domain knowledge about how important sentences in a legal decision relate to a representation of its document structure. For example, analysis of a legal case summarization dataset demonstrates that sentences serving different types of argumentative roles in the decision appear in different sections of the document. In this work, we propose an unsupervised graph-based ranking model that uses a reweighting algorithm to exploit properties of the document structure of legal case decisions. We also explore the impact of using different methods to compute the document structure. Results on the Canadian Legal Case Law dataset show that our proposed method outperforms several strong baselines.

翻译：虽然许多算法可以用于自动总结法律案例裁决,但大多数算法未能将关于法律裁决中重要判决与文件结构代表关系的重要判决的域知识纳入法律裁决中,例如,对法律案例汇总数据集的分析表明,在裁决中起到不同类型争论作用的徒刑出现在文件的不同章节中。在这项工作中,我们提出了一个未经监督的基于图表的排名模型,利用重估算法来利用法律案例裁决文件结构的特性。我们还探讨了使用不同方法计算文件结构的影响。关于加拿大法律案例法数据集的研究结果表明,我们拟议的方法超过了几个强有力的基线。