Summary of Low-resource Court Judgment Summarization For Common Law Systems, by Shuaiqi Liu et al.
Low-Resource Court Judgment Summarization for Common Law Systems
by Shuaiqi Liu, Jiannong Cao, Yicong Li, Ruosong Yang, Zhiyuan Wen
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for generating high-quality summaries of court judgment documents is proposed in this paper. The goal is to facilitate legal practitioners and the general public in accessing how courts operate and how laws are applied. To achieve this, a dataset called CLSum is presented, which contains judgments from multiple common law jurisdictions. This dataset addresses the lack of labeled data for summarizing precedents across multiple jurisdictions. Additionally, large language models (LLMs) are adopted for data augmentation, summary generation, and evaluation. The paper presents an LLM-based data augmentation method incorporating legal knowledge and a legal knowledge enhanced evaluation metric based on LLM to assess the quality of generated judgment summaries. Experimental results demonstrate that LLM-based summarization methods can perform well in few-shot and zero-shot settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps judges make better decisions by creating a system to summarize court judgments from all over the world. Right now, there isn’t enough data for computers to learn how to do this. The researchers created a special dataset called CLSum that has judgment summaries from many countries. They also used big language models (LLMs) to help computers generate good summaries and evaluate their quality. The results show that using LLMs can help computers make accurate judgments even with limited data. |
Keywords
» Artificial intelligence » Data augmentation » Few shot » Summarization » Zero shot
