CaseSumm: A Large-Scale Dataset for Long-Context Summarization from U.S. Supreme Court Opinions

TL;DR

CaseSumm introduces the largest legal case summarization dataset with long-context opinions and evaluates LLMs, revealing limitations of automatic metrics and emphasizing the importance of human assessment.

Contribution

The paper presents a new large-scale legal summarization dataset and provides a comprehensive evaluation of LLM-generated summaries, highlighting challenges in automatic evaluation methods.

Findings

Mistral 7b outperforms larger models on automatic metrics

Human evaluators find GPT-4 summaries clearer and more accurate

Automatic metrics do not correlate well with human judgments

Abstract

This paper introduces CaseSumm, a novel dataset for long-context summarization in the legal domain that addresses the need for longer and more complex datasets for summarization evaluation. We collect 25.6K U.S. Supreme Court (SCOTUS) opinions and their official summaries, known as "syllabuses." Our dataset is the largest open legal case summarization dataset, and is the first to include summaries of SCOTUS decisions dating back to 1815. We also present a comprehensive evaluation of LLM-generated summaries using both automatic metrics and expert human evaluation, revealing discrepancies between these assessment methods. Our evaluation shows Mistral 7b, a smaller open-source model, outperforms larger models on most automatic metrics and successfully generates syllabus-like summaries. In contrast, human expert annotators indicate that Mistral summaries contain hallucinations. The annotators consistently rank GPT-4 summaries as clearer and exhibiting greater sensitivity and specificity. Further, we find that LLM-based evaluations are not more correlated with human evaluations than traditional automatic metrics. Furthermore, our analysis identifies specific hallucinations in generated summaries, including precedent citation errors and misrepresentations of case facts. These findings demonstrate the limitations of current automatic evaluation methods for legal summarization and highlight the critical role of human evaluation in assessing summary quality, particularly in complex, high-stakes domains. CaseSumm is available at https://huggingface.co/datasets/ChicagoHAI/CaseSumm