华南理工大学学报(自然科学版) ›› 2010, Vol. 38 ›› Issue (7): 50-55.doi: 10.3969/j.issn.1000-565X.2010.07.009

• 计算机科学与技术 • 上一篇    下一篇

基于主题词权重和句子特征的自动文摘

蒋昌金1  彭宏1  陈建超2  马千里1   

  1. 1.华南理工大学 计算机科学与工程学院, 广东 广州 510006;2.广东商学院 数学与计算科学学院, 广东 广州 510320
  • 收稿日期:2009-12-04 修回日期:2010-02-28 出版日期:2010-07-25 发布日期:2010-07-25
  • 通信作者: 蒋昌金(1972-),男,博士生,主要从事自然语言处理、人工智能、智能计算等研究. E-mail:jiangchangjin@163.com
  • 作者简介:蒋昌金(1972-),男,博士生,主要从事自然语言处理、人工智能、智能计算等研究.
  • 基金资助:

    广东省自然科学基金资助项目(07006474); 广东省科技攻关项目(2007B010200044)

Automatic Text Summarization Based on Thematic Word Weight and Sentence Features

Jiang Chang-jin1  Peng Hong1  Chen Jian-chao2  Ma Qian-li 1   

  1. 1. School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,Guangdong,China; 2. School of Mathematics and Computational Science,Guangdong University of Business Studies,Guangzhou 510320,Guangdong,China
  • Received:2009-12-04 Revised:2010-02-28 Online:2010-07-25 Published:2010-07-25
  • Contact: 蒋昌金(1972-),男,博士生,主要从事自然语言处理、人工智能、智能计算等研究. E-mail:jiangchangjin@163.com
  • About author:蒋昌金(1972-),男,博士生,主要从事自然语言处理、人工智能、智能计算等研究.
  • Supported by:

    广东省自然科学基金资助项目(07006474); 广东省科技攻关项目(2007B010200044)

摘要: 为获得高质量的自动文摘,在组合词识别算法的基础上,充分考虑词的频率、词性、词的位置、词长等因素,构建了一个词语权重计算公式,该公式能使表达主题的词和短语具有较高的权重.对句子权重的计算,则考虑了句子的内容、位置以及线索词的作用和用户偏好等.摘要的生成充分考虑了候选文摘句的相似性,避免了冗余信息的加入.对摘要的评估进行了从句子粒度到词语粒度的改进,提出了一种基于词语粒度的准确率和召回率计算方法.实验证明,该算法生成的自动文摘有着较高的质量,平均准确率达到77.1%.

关键词: 主题词, 自动文摘, 组合词, 权重计算, 句子特征

Abstract:

In order to generate high-quality automatic text summarization,a formula based on the combined word recognition algorithm is presented to compute the weight of words in a text,with the word frequency,part of speech,word position and word length being considered. By using the proposed formula,a thematic word/phrase is assigned great weight,a sentence is weighted according to its content and position,the cue words in it and the user's preference,and the final summarization is generated by fully considering the similarity of candidate sentences,thus avoiding the information redundance. Moreover,the evaluation approach based on the accuracy and the recall of summerization is improved to increase the computing precision of summarization to the word level instead of the sentence level. Experimental results show that the proposed algorithm generates high-quality summaries,with an average precision of 77. 1% .

Key words: thematic word, automatic text summarization, combined word, weight computing, sentence feature