Low-Resource Court Judgment Summarization for Common Law Systems
Shuaiqi Liu Jiannong Cao Yicong Li Ruosong Yang Zhiyuan Wen
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.