面向普通法系的低资源法院判决摘要生成
Low-Resource Court Judgment Summarization for Common Law Systems
AI总结：
针对普通法系跨多司法管辖区判决摘要生成标注数据稀缺的问题，本研究提出首个多司法管辖区普通法判决摘要数据集CLSum，首次将大语言模型应用于该任务的数据增强、生成与评估，验证了相关方法的有效性。
摘要：
普通法系法院需要参考同类先例的判决来为当前裁判提供依据。生成高质量的法院判决文书摘要能够帮助法律从业者高效查阅过往案件，也能协助普通公众了解法院的运作方式与法律的适用情况。此前的法院判决摘要生成研究主要聚焦于大陆法系或特定司法管辖区的判决，但法官可以参考所有普通法司法管辖区的判决。现有摘要数据集不足以满足跨多司法管辖区的先例摘要生成需求，尤其是在许多司法管辖区标注数据稀缺的情况下。为解决数据集匮乏的问题，我们提出了CLSum——首个用于多司法管辖区普通法法院判决文书摘要生成的数据集。此外，这是首个将大语言模型（LLM）应用于数据增强、摘要生成和评估的法院判决摘要生成研究。具体而言，我们设计了一种融合法律知识的基于LLM的数据增强方法，还提出了一种基于LLM的法律知识增强评估指标，用于评估生成的判决摘要质量。我们的实验结果证实，基于LLM的摘要生成方法在少样本和零样本设置下均能取得良好效果，我们提出的基于LLM的数据增强方法能够缓解低数据资源带来的影响。此外，我们开展了全面的对比实验，以找出能够提升摘要生成性能的关键模型组件与设置。
英文摘要：
Common law courts need to refer to similar precedents' judgments to inform their current decisions. Generating high-quality summaries of court judgment documents can facilitate legal practitioners to efficiently review previous cases and assist the general public in accessing how the courts operate and how the law is applied. Previous court judgment summarization research focuses on civil law or a particular jurisdiction's judgments. However, judges can refer to the judgments from all common law jurisdictions. Current summarization datasets are insufficient to satisfy the demands of summarizing precedents across multiple jurisdictions, especially when labeled data are scarce for many jurisdictions. To address the lack of datasets, we present CLSum, the first dataset for summarizing multi-jurisdictional common law court judgment documents. Besides, this is the first court judgment summarization work adopting large language models (LLMs) in data augmentation, summary generation, and evaluation. Specifically, we design an LLM-based data augmentation method incorporating legal knowledge. We also propose a legal knowledge enhanced evaluation metric based on LLM to assess the quality of generated judgment summaries. Our experimental results verify that the LLM-based summarization methods can perform well in the few-shot and zero-shot settings. Our LLM-based data augmentation method can mitigate the impact of low data resources. Furthermore, we carry out comprehensive comparative experiments to find essential model components and settings that are capable of enhancing summarization performance.
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。
