基于图卷积网络的专利摘要自动生成研究  

Automatic Generation of Patent Summarization Based on Graph Convolution Network

作　　者：李健智 王红玲[1] 王中卿[1] LI Jian-zhi;WANG Hong-ling;WANG Zhong-qing(School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China)

机构地区：[1]苏州大学计算机科学与技术学院,江苏苏州215006

出　　处：《计算机科学》2022年第S01期172-177,共6页Computer Science

基　　金：国家自然科学基金(61976146);国家自然科学基金青年基金项目(61806137)。

摘　　要：专利说明书含有大量有用的信息,但由于篇幅很长,人们很难快速获取其中的有效信息。专利摘要是对一份完整专利说明书的总结与概述,权利要求书作为说明书的一部分,其记载的内容确定了专利申请文件的保护范围,含有专利文献的主要信息。同时经研究发现,专利的权利要求书具有特殊的结构。因此,提出了一种基于图卷积网络的专利摘要自动生成方法,旨在通过专利的权利要求书及其结构信息来生成专利摘要。该方法首先获取权利要求书中的结构信息,在编码阶段引入图卷积神经网络来融合语义信息和结构信息,从而生成高质量的专利摘要。实验结果表明,与目前主流的抽取式摘要方法和传统的编码器-解码器生成方法相比,该方法在ROUGE评价指标上有显著提高。The patent specification contains much useful information.However,due to the long space,it is difficult to obtain effective information quickly.Patent summarization is a summary of a complete patent specification.The right-claiming document determines the scope of protection of the patent application documents.It found that there is a special structure in the right-clai-ming document.Therefore,this paper proposes a method of automatic generation of patent summarization based on graph convolution network.The patent summarization is generated through the patent right-claiming document and its structural information.Firstly,this model obtains patent structural information,and the graph convolution neural network is introduced in the encoder to fuse the serialization information and structural information,to improve the quality of summarization.Experimental results show that this method has a significant improvement in ROUGE evaluation compared with the current main stream extractive summarization method and the traditional encoder-decoder abstractive summarization.

关 键 词：专利摘要 权利要求书 图卷积网络 自动文摘 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]