摘要
提出了一个通过建立段落向量空间模型,根据遗传算法进行文本主题划分的算法,解决了文章的篇章结构分析问题,使得多主题文章的文摘更具内容全面性与结构平衡性。实验结果表明,该算法对多主题文章的主题划分准确率为89.3%,对单主题文章的主题划分准确率为94.6%。
This paper establishes VSM for the whole article based on paragraph, then prnpnses an idea for multi-topic text partitioning based on GA. It solves the prnblem of chapter structural analysis in multi-topic article and makes the abstract of the multi-topic to have more general content and more balanced structure. The experiment on close test shows that the precision of topic partition for multi-topic text and single-topic text reaches 89.3% and 94.6% respectively.
出处
《计算机工程》
EI
CAS
CSCD
北大核心
2006年第11期209-210,218,共3页
Computer Engineering
关键词
自动文摘
向量空间模型
遗传算法
主题划分
Automatic abstraction
Vector space model
Genetic algorithm（GA）
Topic segmentation
