计算机科学 ›› 2011, Vol. 38 ›› Issue (5): 181-185.

• 人工智能 • 上一篇    下一篇

基于无向图构建策略的主题句抽取

葛斌,李芳芳,李阜,肖卫东   

  1. (国防科技大学C4ISR技术国防科技重点实验室 长沙410073)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受国家自然科学基金项目(60903225,60172012)资助。

Subject Sentence Extraction Based on Undirected Graph Construction

GE Bin,LI Fang-fang,LI Fu,XIAO Wei-dong   

  • Online:2018-11-16 Published:2018-11-16

摘要: 基于文档句构建无向图,将主题句的抽取问题转换为无向图中节点的权重计算问题。首先利用滑窗方法抽取主题词,构建空间向量并生成无向图,然后基于向量空间模型计算边权重,最后利用文档句相似度矩阵的权重模型对文档句权重进行建模与计算,依据压缩比得到文档的主题句。实验表明,该方法在不同的压缩比下生成的摘要质量高,主题句抽取结果接近于人工摘要,召回率和准确率综合指数较高。

关键词: 主题句抽取,无向图,文档句权重,自动文摘

Abstract: Undirected graph based on the sentence was proposed. The problem of sentence extraction was transformed to computing undirected graph node weights. This paper first proposed sliding window-based keywords extraction algorithm,followed by the establishment of the undirected graph. The edge weights of the graph were modeled by the Vecfor Space Model(VSM) in turn. The node weights were computed finally by the weight model based on the similarity matrix,and the subject sentences were obtained on the ratio of compression. Experiments show that the proposed automatic summarization techniques improve the recall rate and accuracy effectively.

Key words: Subject sentence extraction, Undirected graph, Sentence weight, Automatic text summarisation

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!