摘要
随着互联网上的信息呈爆炸式增长,如何从海量信息中提取有用信息成了一个关键的技术问题。文本摘要技术能够从大数据中压缩提炼出精炼简洁的文档信息,有效降低用户的信息过载问题,成为研究热点。分类整理分析了近些年来国内外的文本摘要方法及其具体实现,将传统方法和深度学习摘要方法的优缺点进行了对比分析,并对今后的研究方向进行了合理展望。
With the explosive growth of information on the Internet,how to extract useful information from massive information has become a key technical issue.The text summarization technology can compress and extract refined and concise document information from big data,effectively reducing the user information overload problem,and it has become a research hotspot.The domestic and foreign text summarization methods and their concrete realization in recent years were analyzed,the advantages and disadvantages between traditional methods and deep learning summary methods were compared,and a reasonable outlook for future research directions was made.
作者
明拓思宇
陈鸿昶
MING Tuosiyu;CHEN Hongchang(National Digital Switching System Engineering&Technological R&D Center,Zhengzhou 450002,China)
出处
《网络与信息安全学报》
2018年第6期1-10,共10页
Chinese Journal of Network and Information Security
基金
国家自然科学基金青年科学资助项目(No.61601513)~~
关键词
大数据
文本摘要
机器学习
传统方法
深度学习
big data
text summarization
machine learning
traditional methods
deep learning
