LegalViz: Legal Text Visualization by Text To Diagram Generation

• URL: http://arxiv.org/abs/2502.06147v2
• Date: Thu, 13 Feb 2025 05:37:45 GMT
• Title: LegalViz: Legal Text Visualization by Text To Diagram Generation
• Authors: Eri Onami, Taiki Miyanishi, Koki Maeda, Shuhei Kurita,
• Abstract summary: We propose a novel dataset of LegalViz with 23 languages and 7,010 cases of legal document and visualization pairs. LegalViz provides a simple diagram from a complicated legal corpus identifying legal entities, transactions, legal sources, and statements at a glance.
• Score: 5.661933185474446
• License:
• Abstract: Legal documents including judgments and court orders require highly sophisticated legal knowledge for understanding. To disclose expert knowledge for non-experts, we explore the problem of visualizing legal texts with easy-to-understand diagrams and propose a novel dataset of LegalViz with 23 languages and 7,010 cases of legal document and visualization pairs, using the DOT graph description language of Graphviz. LegalViz provides a simple diagram from a complicated legal corpus identifying legal entities, transactions, legal sources, and statements at a glance, that are essential in each judgment. In addition, we provide new evaluation metrics for the legal diagram visualization by considering graph structures, textual similarities, and legal contents. We conducted empirical studies on few-shot and finetuning large language models for generating legal diagrams and evaluated them with these metrics, including legal content-based evaluation within 23 languages. Models trained with LegalViz outperform existing models including GPTs, confirming the effectiveness of our dataset.

Related papers

• 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. 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)
• Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges [4.548047308860141]
  Natural Language Processing is revolutionizing the way legal professionals and laypersons operate in the legal field. This survey follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, reviewing 148 studies, with a final selection of 127 after manual filtering. It explores foundational concepts related to Natural Language Processing in the legal domain.
  arXiv  Detail & Related papers  (2024-10-25T01:17:02Z)
• 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)
• ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models [92.60282074937305]
  We introduce ConTextual, a novel dataset featuring human-crafted instructions that require context-sensitive reasoning for text-rich images. We conduct experiments to assess the performance of 14 foundation models and establish a human performance baseline. We observe a significant performance gap of 30.8% between GPT-4V and human performance.
  arXiv  Detail & Related papers  (2024-01-24T09:07:11Z)
• Constructing a Knowledge Graph for Vietnamese Legal Cases with Heterogeneous Graphs [5.168558598888541]
  This paper presents a knowledge graph construction method for legal case documents and related laws. Our approach consists of three main steps: data crawling, information extraction, and knowledge graph deployment.
  arXiv  Detail & Related papers  (2023-09-16T18:31:47Z)
• Natural Language Decompositions of Implicit Content Enable Better Text Representations [56.85319224208865]
  We introduce a method for the analysis of text that takes implicitly communicated content explicitly into account. We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed. Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP.
  arXiv  Detail & Related papers  (2023-05-23T23:45:20Z)
• Unlocking Practical Applications in Legal Domain: Evaluation of GPT for Zero-Shot Semantic Annotation of Legal Texts [0.0]
  We evaluate the capability of a state-of-the-art generative pre-trained transformer (GPT) model to perform semantic annotation of short text snippets. We found that the GPT model performs surprisingly well in zero-shot settings on diverse types of documents.
  arXiv  Detail & Related papers  (2023-05-08T01:55:53Z)
• 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)
• FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations [114.94628499698096]
  We propose FactGraph, a method that decomposes the document and the summary into structured meaning representations (MRs) MRs describe core semantic concepts and their relations, aggregating the main content in both document and summary in a canonical form, and reducing data sparsity. Experiments on different benchmarks for evaluating factuality show that FactGraph outperforms previous approaches by up to 15%.
  arXiv  Detail & Related papers  (2022-04-13T16:45:33Z)
• LeSICiN: A Heterogeneous Graph-based Approach for Automatic Legal Statute Identification from Indian Legal Documents [10.059041122060686]
  Legal Statute Identification (LSI) aims to identify the legal statutes that are relevant to a given description of Facts or evidence of a legal case. Existing methods only utilize the textual content of Facts and legal articles to guide such a task. We take the first step towards utilising both the text and the legal citation network for the LSI task.
  arXiv  Detail & Related papers  (2021-12-29T18:39:35Z)
• LexGLUE: A Benchmark Dataset for Legal Language Understanding in English [15.026117429782996]
  We introduce the Legal General Language Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.
  arXiv  Detail & Related papers  (2021-10-03T10:50:51Z)

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.