@inproceedings{t-y-s-s-etal-2025-lexgenie,
    title = "{L}ex{G}enie: Automated Generation of Structured Reports for {E}uropean Court of Human Rights Case Law",
    author = "T.y.s.s, Santosh  and
      Aly, Mahmoud  and
      Ichim, Oana  and
      Grabmair, Matthias",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-industry.47/",
    pages = "672--683",
    ISBN = "979-8-89176-288-6",
    abstract = "Analyzing large volumes of case law to uncover evolving legal principles, across multiple cases, on a given topic is a demanding task for legal professionals. Structured topical reports provide an effective solution by summarizing key issues, principles, and judgments, enabling comprehensive legal analysis on a particular topic. While prior works have advanced query-based individual case summarization, none have extended to automatically generating multi-case structured reports. To address this, we introduce LexGenie, an automated LLM-based pipeline designed to create structured reports using the entire body of case law on user-specified topics within the European Court of Human Rights jurisdiction. LexGenie retrieves, clusters, and organizes relevant passages by topic to generate a structured outline and cohesive content for each section. Expert evaluation confirms LexGenie{'}s utility in producing structured reports that enhance efficient, scalable legal analysis."
}