LexGenie: Automated Generation of Structured Reports for European Court of Human Rights Case Law

• URL: http://arxiv.org/abs/2503.03266v1
• Date: Wed, 05 Mar 2025 08:49:28 GMT
• Title: LexGenie: Automated Generation of Structured Reports for European Court of Human Rights Case Law
• Authors: T. Y. S. S Santosh, Mahmoud Aly, Oana Ichim, Matthias Grabmair,
• Abstract summary: LexGenie is an automated pipeline designed to create structured reports using the entire body of case law on user-specified topics.<n>It retrieves, clusters, and organizes relevant passages by topic to generate a structured outline and cohesive content for each section.<n>Expert evaluation confirms LexGenie's utility in producing structured reports that enhance efficient, scalable legal analysis.
• Score: 1.474945380093949
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SyLeR: A Framework for Explicit Syllogistic Legal Reasoning in Large Language Models [5.501226256903341]
  We propose SyLeR, a novel framework that empowers LLMs to engage in explicit syllogistic legal reasoning. SyLeR integrates a tree-structured hierarchical retrieval mechanism to effectively combine relevant legal statutes and precedent cases.
  arXiv  Detail & Related papers  (2025-04-05T03:34:51Z)
• A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences [76.73731245899454]
  We propose a transparent law reasoning schema enriched with hierarchical factum probandum, evidence, and implicit experience.<n>Inspired by this schema, we introduce the challenging task, which takes a textual case description and outputs a hierarchical structure justifying the final decision.<n>This benchmark paves the way for transparent and accountable AI-assisted law reasoning in the Intelligent Court''
  arXiv  Detail & Related papers  (2025-03-02T10:26:54Z)
• AnnoCaseLaw: A Richly-Annotated Dataset For Benchmarking Explainable Legal Judgment Prediction [56.797874973414636]
  AnnoCaseLaw is a first-of-its-kind dataset of 471 meticulously annotated U.S. Appeals Court negligence cases.<n>Our dataset lays the groundwork for more human-aligned, explainable Legal Judgment Prediction models.<n>Results demonstrate that LJP remains a formidable task, with application of legal precedent proving particularly difficult.
  arXiv  Detail & Related papers  (2025-02-28T19:14:48Z)
• Knowledge Graphs Construction from Criminal Court Appeals: Insights from the French Cassation Court [49.1574468325115]
  This paper presents a framework for constructing knowledge graphs from appeals to the French Cassation Court.<n>The framework includes a domain-specific ontology and a derived dataset, offering a foundation for structured legal data representation and analysis.
  arXiv  Detail & Related papers  (2025-01-24T15:38:32Z)
• LexAbSumm: Aspect-based Summarization of Legal Decisions [1.3723120574076126]
  LexAbSumm is a dataset designed for aspect-based summarization of legal case decisions, sourced from the European Court of Human Rights jurisdiction. We evaluate several abstractive summarization models tailored for longer documents on LexAbSumm, revealing a challenge in conditioning these models to produce aspect-specific summaries.
  arXiv  Detail & Related papers  (2024-03-31T08:00:40Z)
• DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
  We introduce DELTA, a discriminative model designed for legal case retrieval. We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability. Our approach can outperform existing state-of-the-art methods in legal case retrieval.
  arXiv  Detail & Related papers  (2024-03-27T10:40:14Z)
• A Deep Learning-Based System for Automatic Case Summarization [2.9141777969894966]
  This paper presents a deep learning-based system for efficient automatic case summarization. The system offers both supervised and unsupervised methods to generate concise and relevant summaries of lengthy legal case documents. Future work will focus on refining summarization techniques and exploring the application of our methods to other types of legal texts.
  arXiv  Detail & Related papers  (2023-12-13T01:18:10Z)
• MUSER: A Multi-View Similar Case Retrieval Dataset [65.36779942237357]
  Similar case retrieval (SCR) is a representative legal AI application that plays a pivotal role in promoting judicial fairness. Existing SCR datasets only focus on the fact description section when judging the similarity between cases. We present M, a similar case retrieval dataset based on multi-view similarity measurement and comprehensive legal element with sentence-level legal element annotations.
  arXiv  Detail & Related papers  (2023-10-24T08:17:11Z)
• SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval [75.05173891207214]
  Legal case retrieval plays a core role in the intelligent legal system. Most existing language models have difficulty understanding the long-distance dependencies between different structures. We propose a new Structure-Aware pre-traIned language model for LEgal case Retrieval.
  arXiv  Detail & Related papers  (2023-04-22T10:47:01Z)
• Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions [7.99536002595393]
  We propose an unsupervised graph-based ranking model that uses a reweighting algorithm to exploit document structure. Results on the Canadian Legal Case Law dataset show that our proposed method outperforms several strong baselines.
  arXiv  Detail & Related papers  (2022-11-06T22:20:42Z)
• Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents [7.6340456946456605]
  We propose an unsupervised summarization algorithm DELSumm for summarizing legal case documents. Our proposed algorithm outperforms several supervised summarization models that are trained over thousands of document-summary pairs.
  arXiv  Detail & Related papers  (2021-06-30T08:06:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.