Scholarlink

<p>This paper explores advancements in Natural Language Processing (NLP) for legal lay summarization by systematically analyzing existing methodologies, datasets, and research findings. We review current literature, highlighting key challenges such as data scarcity and the complexity of legal language. A primary contribution of this study is the development of LegalEase, a specialized dataset designed to improve model training for summarizing legal documents in layman’s terms. Our findings demonstrate that subdomain-specific datasets within the legal domain outperform general legal datasets in enhancing NLP model performance for generating accurate and comprehensible legal summaries. The insights and methodologies presented provide a foundation for future research in legal lay summarization.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Legal lay summarization: exploring methods and data generation with large language models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

查看全文
  • Gianluca Moro,
  • Leonardo David Matteo Magnani,
  • Luca Ragazzi

摘要

This paper explores advancements in Natural Language Processing (NLP) for legal lay summarization by systematically analyzing existing methodologies, datasets, and research findings. We review current literature, highlighting key challenges such as data scarcity and the complexity of legal language. A primary contribution of this study is the development of LegalEase, a specialized dataset designed to improve model training for summarizing legal documents in layman’s terms. Our findings demonstrate that subdomain-specific datasets within the legal domain outperform general legal datasets in enhancing NLP model performance for generating accurate and comprehensible legal summaries. The insights and methodologies presented provide a foundation for future research in legal lay summarization.