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.

通过使用无监督的基于图形的排名模型，并使用重新加权算法来利用法律判决文件结构的特性，本研究研究了使用不同方法计算文档结构的影响，结果在加拿大法律案例实验数据集上表明了我们提出的方法优于多个强基线。