Abstract: This paper establishes VSM for the whole article based on paragraph, then proposes an idea for multi-topic text partitioning based on GA. It solves the problem 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.

Key words: Automatic abstraction; Vector space model; Genetic algorithm(GA); Topic segmentation

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%。

关键词: 自动文摘；向量空间模型；遗传算法；主题划分