摘要
根据新闻文本的特点,分别对新闻标题与正文进行分析,该文提出了一种针对新闻文本的特征加权的主题句抽取方法。首先对新闻主题句在文本中的分布情况进行分析,选取了位置特征;然后根据新闻标题对于新闻主旨的提示作用,选取了标题句子重合度与关联度的特征,且在关联度特征中将基于加权二部图的最大匹配算法融入其中;最后依据句子的得分排名,进行主题句抽取。实验显示,利用该方法进行主题句抽取的P@1为75.9%,P@3达到92.4%。
A topic sentence extraction method for news text is proposed.Firstly,the location feature is derived from the distribution of news topic sentence in the text.Then,the overlap ratio between a sentence and the title calculated owing to the interrelation of the news title with the theme.To best estimate the relevancy between the title and the candidate topic sentence,a maximum matching based on weighted bipartite graph is applied.Finally,the topic sentence is selected according to the sentence rank score.The experimental results show that the proposed method reaches 75.9%in P@1,and 92.4%in P@3.
出处
《中文信息学报》
CSCD
北大核心
2017年第5期120-126,共7页
Journal of Chinese Information Processing
基金
沈阳省自然科学基金(20170540696)
沈阳市科技计划项目(17-231-1-82)
关键词
特征加权
重合度
关联度
加权二部图
feature weighted
overlap ratio
relevancy degree
weighted bipartite graph
