Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Soft Computing Exploration
Yuni Yamasari
Department of Informatics Engineering, Universitas Negeri Surabaya, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Efficient hierarchical summarization of long legal documents using a lightweight transformer and divide and conquer strategy Muhammad Zhafran Ammar; Ricky Eka Putra; Yuni Yamasari
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.5

Abstract

This research addresses the challenges of summarizing long and complex legal documents, which often exceed the input length limitations of transformer-based models and contain intricate legal reasoning structures. The purpose of this study is to develop an efficient and scalable summarization framework that preserves semantic fidelity and structural coherence in judicial summaries. To achieve this objective, a hybrid summarization pipeline is proposed by integrating a Bidirectional Encoder Representations from Transformers (BERT)-based extractive model with a hierarchical abstractive model based on Distilled Bidirectional and Auto-Regressive Transformers (DistilBART), combined with a Divide-and-Conquer strategy. The proposed method partitions long legal documents into smaller segments, processes each segment independently, and reconstructs them into a coherent final summary. Experiments were conducted on the Indian Legal Case Summarization dataset and evaluated using Recall-Oriented Understudy for Gisting Evaluation (ROUGE), BERTScore, and Cosine Similarity to assess both lexical overlap and semantic similarity. The results show that the hierarchical DistilBART model outperforms the extractive baseline, achieving a ROUGE-1 score of 0.3802 and a Cosine Similarity of 0.6917. These findings demonstrate that the proposed framework provides an effective solution for long-document summarization in the legal domain.
Co-Authors Muhammad Zhafran Ammar Ricky Eka Putra