论文标题
计算和利用文档结构，以改善法律案例决策的无监督提取性汇总
Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions
论文作者
论文摘要
尽管可以使用许多算法来自动汇总法律案件的决策，但大多数人未能将有关法律决定中重要句子与其文档结构的表示有关的域知识纳入知识。例如，对法律案例摘要数据集的分析表明，在该决定中担任不同类型的论证角色的句子出现在文档的不同部分中。在这项工作中，我们提出了一个无监督的基于图的排名模型，该模型使用重新加权算法利用法律案例决策的文档结构的属性。我们还探讨了使用不同方法计算文档结构的影响。加拿大法律案例法数据集的结果表明，我们提出的方法的表现优于几个强大的基线。
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.
