PersonaMatrix: 一种面向人物的法律摘要评估配方
PersonaMatrix: A Recipe for Persona-Aware Evaluation of Legal Summarization
AI总结：
本文提出PersonaMatrix框架，通过六个角色评估法律摘要，引入控制的维度转移试点数据集，以暴露法律摘要在人物意识和非人物意识法官间的差异最优解。
摘要：
法律文件通常长、密集且难以理解，不仅对普通公众，也对法律专家来说都是挑战。尽管自动文档摘要有潜力提高法律知识的可及性，但现有任务导向评估器忽略了用户和利益相关者的多样化需求。需要开发工具，使案件摘要的技术性适合律师，同时对自助公众研究其诉讼而言又易于访问。我们介绍了PersonaMatrix，一种通过六个角色（包括法律和非法律用户）评估摘要的框架。我们还引入了一个受控的维度转移试点数据集，包含美国民事权利案件摘要，沿深度、可访问性、程序细节以及多样性-覆盖指数（DCI）变化，以揭示法律摘要在人物意识和非人物意识法官之间的差异最优解。本工作使法律AI摘要系统能够为专家和非专家用户进行优化，有望提高法律知识的可及性。代码库和数据在GitHub上公开。
英文摘要：
Legal documents are often long, dense, and difficult to comprehend, not only for laypeople but also for legal experts. While automated document summarization has great potential to improve access to legal knowledge, prevailing task-based evaluators overlook divergent user and stakeholder needs. Tool development is needed to encompass the technicality of a case summary for a litigator yet be accessible for a self-help public researching for their lawsuit. We introduce PersonaMatrix, a persona-by-criterion evaluation framework that scores summaries through the lens of six personas, including legal and non-legal users. We also introduce a controlled dimension-shifted pilot dataset of U.S. civil rights case summaries that varies along depth, accessibility, and procedural detail as well as Diversity-Coverage Index (DCI) to expose divergent optima of legal summary between persona-aware and persona-agnostic judges. This work enables refinement of legal AI summarization systems for both expert and non-expert users, with the potential to increase access to legal knowledge. The code base and data are publicly available in GitHub.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
