摘要
对面向查询的自动文本摘要技术进行系统梳理,分析所用方法的基本思想、优缺点,并总结未来的发展方向。通过分析梳理,总结出了四大类面向查询的自动文本摘要技术:基于图模型的方法、基于机器学习的方法、基于聚类的方法和其他方法。在今后的研究过程中,基于神经网络和多模型融合的方法将成为未来研究的热点,在应用层面上,与实际应用场景相结合的算法研究将成为趋势。
This paper systematically combed the query-oriented automatic summarization technology,analyzed the basic ideas,advantages and disadvantages of the methods used,and summarized the future development direction.By analyzing,four kinds of query-oriented automatic summarization were summarized:the method based on graph model,the method based on machine learning,the method based on clustering and other methods.In the future,the method based on neural network and multi model fusion will become the focus of future research.In the application level,it will become a trend to study the algorithm combining with the actual application scene.
作者
王凯祥
WANG Kai-xiang(School of Information Resource Management,Renmin University of China,Beijing 100872,China)
出处
《计算机科学》
CSCD
北大核心
2018年第B11期12-16,共5页
Computer Science
关键词
文本摘要
图模型
流排序
主题模型
神经网络
Summarization
Graph model
Manifold ranking
Topic model,Neural network
