用事件结构化规划提升法律判决摘要的准确性
CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization
- 以事件三元组构建法律文本的叙事结构
- 在4个数据集上优于传统实体中心方法
- 适合法律AI、智能文书系统开发者
法律从业者常面临冗长判决书难以快速理解的问题。为解决此挑战，本文研究了在法律案件摘要中引入结构化规划的必要性，尤其关注反映法律文档叙事性的事件中心表示。提出CoPERLex框架，分三步进行：首先内容筛选，识别判决书中关键信息；其次利用选中内容生成基于事件中心表示的中间计划，形式为“主语-动词-宾语”三元组；最后结合内容与结构化计划生成连贯摘要。在四个法律摘要数据集上的实验表明，整合内容筛选与规划组件显著有效，事件中心计划相比传统实体中心方法在法律判决场景中更具优势。
原文摘要 · Abstract (English)
Legal professionals often struggle with lengthy judgments and require efficient summarization for quick comprehension. To address this challenge, we investigate the need for structured planning in legal case summarization, particularly through event-centric representations that reflect the narrative nature of legal case documents. We propose our framework, CoPERLex, which operates in three stages: first, it performs content selection to identify crucial information from the judgment; second, the selected content is utilized to generate intermediate plans through event-centric representations modeled as Subject-Verb-Object tuples; and finally, it generates coherent summaries based on both the content and the structured plan. Our experiments on four legal summarization datasets demonstrate the effectiveness of integrating content selection and planning components, highlighting the advantages of event-centric plans over traditional entity-centric approaches in the context of legal judgements.
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