Translating Legalese: Enhancing Public Understanding of Court Opinions with Legal Summarizers
arXiv:2311.06534 · doi:10.1145/3614407.3643700
Abstract
Judicial opinions are written to be persuasive and could build public trust in court decisions, yet they can be difficult for non-experts to understand. We present a pipeline for using an AI assistant to generate simplified summaries of judicial opinions. Compared to existing expert-written summaries, these AI-generated simple summaries are more accessible to the public and more easily understood by non-experts. We show in a survey experiment that the AI summaries help respondents understand the key features of a ruling, and have higher perceived quality, especially for respondents with less formal education.
published in proceedings of CSLAW 2024: Symposium on Computer Science and Law
References in corpus (8)
- Large Language Models are Zero-Shot Reasoners
- News Summarization and Evaluation in the Era of GPT-3
- An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics
- Recursively Summarizing Books with Human Feedback
- Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures
- Summarization is (Almost) Dead
- Sentence Simplification via Large Language Models
- Legal Extractive Summarization of U.S. Court Opinions