A Hybrid Summarization Model for Legal Judgment Document Based on Domain Knowledge

Abstract

Legal judgment document summarization, as a task specific to the legal domain, involves automatically generating a fluent, informative, and well-organized summary from the original legal judgment document. Unlike traditional text summarization tasks, this domain-specific task places higher demands on content accuracy and completeness in the summary, while also requiring the preservation of the professional expression found in the original text. Consequently, conventional summarization methods often struggle to perform effectively in the legal domain. In response to this challenge, this paper introduces a hybrid summarization model tailored for legal judgment documents. Our model harnesses the strengths of both extractive and abstractive summarization methods, incorporating domain knowledge to enhance the summary generation process. We conduct extensive experiments to verify the effectiveness of our proposed method and compare the results with a baseline using ROUGE evaluation metrics. The experimental findings highlight that our model excels in providing more accurate and readable summarizations compared to traditional methods.