集成技术2024，Vol.13Issue(1)：62-71,10.DOI:10.12146/j.issn.2095-3135.20230209001
面向中文法律裁判文书的抽取式摘要算法
Extractive Summarization Algorithm for Chinese Legal Judgment Documents
摘要
Abstract
The purpose of automatic judgment document summarization is to allow computers to automatically select,extract,and compress important information from legal texts so as to reduce workload of practitioners.Currently,most summarization algorithms based on pre-trained language models have limitations on the length of the input text,so they cannot effectively summarize long texts.In this thesis,an innovative extractive summarization algorithm is introduced,which uses a pre-trained language model to generate sentence vectors.Based on the Transformer encoder structure,the summarization task can be completed by fused information including sentence vectors,position and length of sentences.Experimental results showed that,the algorithm can effectively handle the task of summarizing long texts.In addition,the model was tested on the 2020 CAIL(challenge of AI in law)summarization dataset,and results showed that compared to the baseline model,the proposed model showed significant improvement in the ROUGE-1,ROUGE-2,and ROUGE-L metrics.关键词
抽取式摘要模型/法律裁判文书/文本自动摘要/深度神经网络Key words
extractive summarization model/legal judgment documents/automatic text summarization/deep neural network分类
信息技术与安全科学引用本文复制引用
温嘉宝,杨敏..面向中文法律裁判文书的抽取式摘要算法[J].集成技术,2024,13(1):62-71,10.基金项目
深圳市基础研究重点项目(JCYJ20210324115614039) This work is supported by Shenzhen Basic Research Foundation(JCYJ20210324115614039) （JCYJ20210324115614039）
