Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection

• URL: http://arxiv.org/abs/2507.18952v1
• Date: Fri, 25 Jul 2025 04:39:33 GMT
• Title: Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection
• Authors: Yongjie Li, Ruilin Nong, Jianan Liu, Lucas Evans,
• Abstract summary: Legal document summarization represents a significant advancement towards improving judicial efficiency.<n>This research highlights promising technology-driven strategies that can significantly alter workflow dynamics within the legal sector.
• Score: 14.157213827899342
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Legal document summarization represents a significant advancement towards improving judicial efficiency through the automation of key information detection. Our approach leverages state-of-the-art natural language processing techniques to meticulously identify and extract essential data from extensive legal texts, which facilitates a more efficient review process. By employing advanced machine learning algorithms, the framework recognizes underlying patterns within judicial documents to create precise summaries that encapsulate the crucial elements. This automation alleviates the burden on legal professionals, concurrently reducing the likelihood of overlooking vital information that could lead to errors. Through comprehensive experiments conducted with actual legal datasets, we demonstrate the capability of our method to generate high-quality summaries while preserving the integrity of the original content and enhancing processing times considerably. The results reveal marked improvements in operational efficiency, allowing legal practitioners to direct their efforts toward critical analytical and decision-making activities instead of manual reviews. This research highlights promising technology-driven strategies that can significantly alter workflow dynamics within the legal sector, emphasizing the role of automation in refining judicial processes.

Related papers

• Does Machine Unlearning Truly Remove Model Knowledge? A Framework for Auditing Unlearning in LLMs [58.24692529185971]
  We introduce a comprehensive auditing framework for unlearning evaluation comprising three benchmark datasets, six unlearning algorithms, and five prompt-based auditing methods.<n>We evaluate the effectiveness and robustness of different unlearning strategies.
  arXiv  Detail & Related papers  (2025-05-29T09:19:07Z)
• Tasks and Roles in Legal AI: Data Curation, Annotation, and Verification [4.099848175176399]
  The application of AI tools to the legal field feels natural.<n>However, legal documents differ from the web-based text that underlies most AI systems.<n>We identify three areas of special relevance to practitioners: data curation, data annotation, and output verification.
  arXiv  Detail & Related papers  (2025-04-02T04:34:58Z)
• Can LLMs Automate Fact-Checking Article Writing? [69.90165567819656]
  We argue for the need to extend the typical automatic fact-checking pipeline with automatic generation of full fact-checking articles.<n>We develop QRAFT, an LLM-based agentic framework that mimics the writing workflow of human fact-checkers.
  arXiv  Detail & Related papers  (2025-03-22T07:56:50Z)
• Learning Task Representations from In-Context Learning [73.72066284711462]
  Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning.<n>We introduce an automated formulation for encoding task information in ICL prompts as a function of attention heads.<n>We show that our method's effectiveness stems from aligning the distribution of the last hidden state with that of an optimally performing in-context-learned model.
  arXiv  Detail & Related papers  (2025-02-08T00:16:44Z)
• Algorithm for Automatic Legislative Text Consolidation [0.0]
  We present a generative approach that processes legislative texts to automatically apply amendments.<n>Our method employs light quantized generative model, fine-tuned with LoRA, to generate accurate and reliable amended texts.<n>A full automated pipeline of legislative text consolidation can be done in a few hours, with a success rate of more than 63% on a difficult bill.
  arXiv  Detail & Related papers  (2025-01-28T08:52:33Z)
• Judgement Citation Retrieval using Contextual Similarity [0.0]
  We propose a methodology that combines natural language processing (NLP) and machine learning techniques to enhance the organization and utilization of legal case descriptions. Our methodology addresses two primary objectives: unsupervised clustering and supervised citation retrieval. Our methodology achieved an impressive accuracy rate of 90.9%.
  arXiv  Detail & Related papers  (2024-05-28T04:22:28Z)
• Empowering Prior to Court Legal Analysis: A Transparent and Accessible Dataset for Defensive Statement Classification and Interpretation [5.646219481667151]
  This paper introduces a novel dataset tailored for classification of statements made during police interviews, prior to court proceedings. We introduce a fine-tuned DistilBERT model that achieves state-of-the-art performance in distinguishing truthful from deceptive statements. We also present an XAI interface that empowers both legal professionals and non-specialists to interact with and benefit from our system.
  arXiv  Detail & Related papers  (2024-05-17T11:22:27Z)
• 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)
• An Uncommon Task: Participatory Design in Legal AI [64.54460979588075]
  We examine a notable yet understudied AI design process in the legal domain that took place over a decade ago. We show how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers.
  arXiv  Detail & Related papers  (2022-03-08T15:46:52Z)
• Generating Fact Checking Explanations [52.879658637466605]
  A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process. This paper provides the first study of how these explanations can be generated automatically based on available claim context. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system.
  arXiv  Detail & Related papers  (2020-04-13T05:23:25Z)

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.