# Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection

arXiv: https://arxiv.org/abs/2507.18952 · HTML: https://mlsanity.com/paper/2507.18952/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 36 of its 41 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 30 of 36)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
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- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
- [WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization](https://mlsanity.com/paper/2010.03093/) (2020)
- [Unifying Vision, Text, and Layout for Universal Document Processing](https://mlsanity.com/paper/2212.02623/) (2022)
- [Unlocking Context Constraints of LLMs: Enhancing Context Efficiency of LLMs with Self-Information-Based Content Filtering](https://mlsanity.com/paper/2304.12102/) (2023)
- [Adapting Large Language Models for Document-Level Machine Translation](https://mlsanity.com/paper/2401.06468/) (2024)
- [DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing](https://mlsanity.com/paper/2410.12189/) (2024)
- [Better Call GPT, Comparing Large Language Models Against Lawyers](https://mlsanity.com/paper/2401.16212/) (2024)
- [Long Text and Multi-Table Summarization: Dataset and Method](https://mlsanity.com/paper/2302.03815/) (2023)
- [Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method](https://mlsanity.com/paper/2401.06782/) (2024)
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- [Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction](https://mlsanity.com/paper/2407.01964/) (2024)
- [ERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and Personalization](https://mlsanity.com/paper/2405.06683/) (2024)
- [Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs](https://mlsanity.com/paper/2410.06581/) (2024)
- [ANLS* -- A Universal Document Processing Metric for Generative Large Language Models](https://mlsanity.com/paper/2402.03848/) (2024)
- [Agents on the Bench: Large Language Model Based Multi Agent Framework for Trustworthy Digital Justice](https://mlsanity.com/paper/2412.18697/) (2024)
- [Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models](https://mlsanity.com/paper/2311.07314/) (2023)
- [WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset](https://mlsanity.com/paper/2305.05432/) (2023)
- [Large Language Model Prompt Chaining for Long Legal Document Classification](https://mlsanity.com/paper/2308.04138/) (2023)
- [A Comprehensive Survey on Machine Learning Driven Material Defect Detection](https://mlsanity.com/paper/2406.07880/) (2024)
- [Intelligent System for Automated Molecular Patent Infringement Assessment](https://mlsanity.com/paper/2412.07819/) (2024)
- [Enhancing Legal Document Retrieval: A Multi-Phase Approach with Large Language Models](https://mlsanity.com/paper/2403.18093/) (2024)
- [The GitHub Development Workflow Automation Ecosystems](https://mlsanity.com/paper/2305.04772/) (2023)
- [Domain-Agnostic Neural Architecture for Class Incremental Continual Learning in Document Processing Platform](https://mlsanity.com/paper/2307.05399/) (2023)
- [NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models](https://mlsanity.com/paper/2309.09070/) (2023)
- [Voice Recognition Robot with Real-Time Surveillance and Automation](https://mlsanity.com/paper/2312.04072/) (2023)
- [HADES: Homologous Automated Document Exploration and Summarization](https://mlsanity.com/paper/2302.13099/) (2023)
- [Hyper-automation-The next peripheral for automation in IT industries](https://mlsanity.com/paper/2305.11896/) (2023)

## Abstract (arXiv)

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.