Overview of Judicial Text Summarization Method

Abstract

This article delves deep into the core aspects of the task of generating judicial text summaries. Through a systematic review and distillation of existing relevant literature, the article primarily focuses on extractive text summarization techniques in both unsupervised and supervised learning contexts, conducting a multidimensional and comprehensive analysis. To begin with, the article traces the evolution of text summarization techniques and dissects the differences between extractive and generative text summarization methods, along with a comparison of various algorithms. Furthermore, it provides a detailed introduction to a pipeline judicial summary generation model that combines both extractive and generative approaches. The article also conducts an in-depth analysis of the impact of transfer learning, using three different models, on judicial text summary generation. Lastly, while acknowledging significant progress in the field, the article points out the main issues and challenges in current judicial text summarization research. It also suggests potential solutions and outlines future development trends.