关键词: 深层词语挖掘, 多文档自动文摘, 逻辑回归模型

Abstract: A multi-document summarization method based on topic and sub-event is proposed.The method extracts eight basic word features using the frequency，position information，word of event and topic information etc.which break through traditional statistical method，then chooses logistic regression model to compute words score.The summarizer gives a score to sentences in term of the word values，and combines score and redundancy of sentence to produce summarization.It uses three different summary systems（Coverage Baseline，Centroid-Based Summary and Word Mining based Summary（WMS）） in three aspects（N-gram co-occurrence statistics，term word coverage and high frequency word） to compare.The experimental results show the system of WMS has more effectiveness and feasibility.

Key words: deeply word mining, multi-document summarization, logistic regression model