RLCPAR:一种基于强化学习的中文专利摘要改写模型  

RLCPAR:A Rewriting Model for Chinese Patent Abstracts Based on Reinforcement Learning

作　　者：张乐 冷基栋 吕学强[1] 崔卓 王磊[1] 游新冬 Zhang Le;Leng Jidong;Lv Xueqiang;Cui Zhuo;Wang Lei;You Xindong(Beijing Key Laboratory of Internet Culture and Digital Dissemination Research,Beijing Information Science&Technology University,Beijing 100101,China;School of Information&Communication Engineering,Beijing Information Science&Technology University,Beijing 100101,China)

机构地区：[1]北京信息科技大学网络文化与数字传播北京市重点实验室,北京100101 [2]北京信息科技大学信息与通信工程学院,北京100101

出　　处：《数据分析与知识发现》2021年第7期59-69,共11页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目(项目编号:61671070);青海省藏文信息处理与机器翻译重点实验室/藏文信息处理教育部重点实验室开放课题基金项目(项目编号:2019Z002)的研究成果之一。

摘　　要：【目的】提出一种基于强化学习的中文专利摘要改写模型(RLCPAR),以解决现有的自动摘要方法在处理多句子摘要改写时存在的句子冗余和准确率低的问题。【方法】引入专利术语词典,运用基于强化学习的句子抽取方法,对专利说明书文本的关键句进行抽取,利用Transformer深度神经网络文本生成方法生成候选摘要,最终融合专利原始摘要信息,经过语义去重和排序得到改写的摘要。【结果】RLCPAR模型实现端到端的专利摘要改写,并且在ROUGE-1、ROUGE-2和ROUGE-L评价标准上分别达到56.95%、37.21%和51.24%。【局限】数据主要来源于中药材领域,在其他领域中的效果有待验证。【结论】RLCPAR模型明显优于其他序列生成方法,改善了中文专利摘要改写的效果。[Objective]This paper proposes a rewriting model for Chinese patent abstracts based on reinforcement learning(RLCPAR),aiming to address the issues of sentence redundancy and low accuracy in rewriting multisentence abstracts.[Methods]First,we used the RLCPAR to extract key sentences from patent descriptions with the help of patent term dictionary and reinforcement learning.Then,we generated the candidate abstracts using the Transformer deep neural network.Finally,we merged the candidate abstracts with the original patent abstracts to obtain the rewritten abstracts after semantic de-duplication and sorting.[Results]The proposed model effectively finished the end-to-end rewriting of patent abstracts.The scores of RLCPAR were 56.95%,37.21%and 51.24%with the ROUGE-1,ROUGE-2 and ROUGE-L criteria.[Limitations]The experimental data,which were mainly on Chinese medicine materials,needs to be expanded to other fields.[Conclusions]The PLCPAR model is much better than other sequence generation methods and improves the rewriting quality of Chinese patent abstracts.

关 键 词：专利摘要 自动改写 强化学习 神经网络 文本生成 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]