基于图排序算法的自动文摘研究综述  

Survey on Graph Model-based Document Summarization

作　　者：王俊丽[1] 魏绍臣 管敏[1] 

机构地区：[1]同济大学电子与信息工程学院CAD中心,上海201804

出　　处：《计算机科学》2015年第12期1-7,39,共8页Computer Science

基　　金：国家自然科学基金(61105047);港澳台科技合作项目(2013DFM10100);上海市科委项目(14JC1405800);国家科技支撑计划(2012BAF12B11)资助

摘　　要：互联网技术的快速发展使得信息的采集和传播速度达到了空前的水平,海量的数据使得人们获取有价值的信息越发困难。自动文摘技术可以从海量的信息中提取出能代表原文重要内容且简洁精练的一段文字,高度压缩文档是解决信息超载问题的有效方法,因此自动文摘技术的研究引起人们越来越多的关注。目前诸如统计分析、机器学习技术以及语言学知识等在已有的自动文摘系统中都有所应用。对基于图排序算法的自动文摘的研究成果进行综述,首先阐述自动文摘以及图排序算法的基本知识,然后重点从图的构建、图排序、句子选择3个方面系统地介绍基于图排序算法的自动文摘的研究现状,最后在分析已有自动文摘系统的基础上,探讨了基于图排序算法的自动文摘的未来发展方向。With the rapid development of the Internet technologies, the speed of information transmission has reached unprecedentedly high levels. However, getting valuable information from mass data is becoming more and more difficult. Automatic summarization technologies allow us to extract a summary that represents the main idea of the original document, which have attracted much attention. Now many related technologies have been widely used in existing automatic summarization approaches, such as statistical analysis, machine learning technology, linguistic knowledge and etc. This paper summarized research works of summarization approaches based on graph-based ranking algorithms. First the basic knowledge of automatic summarization and graph-based ranking algorithms were elaborated. Then the summarization approaches based on graph ranking algorithms were introduced, mainly including three parts： construction of text graph,graph-based ranking,and sentences selection. Finally on the basis of analysis of existing approaches, the future development of graph-based summarization approaches was explored.

关 键 词：自动文摘 图排序 图模型 文本挖掘 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]