网络与信息安全学报 ›› 2018, Vol. 4 ›› Issue (6): 1-10.doi: 10.11959/j.issn.2096-109x.2018048

• 综述 •    下一篇

文本摘要研究进展与趋势

明拓思宇,陈鸿昶   

  1. 国家数字交换系统工程技术研究中心,河南郑州 450002
  • 修回日期:2018-06-01 出版日期:2018-06-15 发布日期:2018-08-08
  • 作者简介:明拓思宇(1994-),男,湖南长沙人,国家数字交换系统工程技术研究中心硕士生,主要研究方向为文本摘要。|陈鸿昶(1964-),男,河南郑州人,国家数字交换系统工程技术研究中心教授、博士生导师,主要研究方向为电信网信息安全。
  • 基金资助:
    国家自然科学基金青年科学资助项目(61601513)

Research progress and trend of text summarization

Tuosiyu MING,Hongchang CHEN   

  1. National Digital Switching System Engineering &Technological R&D Center,Zhengzhou 450002,China
  • Revised:2018-06-01 Online:2018-06-15 Published:2018-08-08
  • Supported by:
    The National Natural Science Foundation of Youth Science(61601513)

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。

关键词: 大数据, 文本摘要, 机器学习, 传统方法, 深度学习

Abstract:

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.

Key words: big data, text summarization, machine learning, traditional methods, deep learning

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