Document Relevance Identifying and its Effect in Query-Focused Text Summarization

There is an important issue that text summarization has to embody personal information need and provide indicative message to user. In this paper, a method of acquiring relevant documents based on user-feedback information and transductive inference SVM machine learning is presented. This method can well avoid the subjectivity of deciding relevant documents empirically. Furthermore, a sentence selection strategy through extracting keywords is proposed. It calculated the word´s query related feature through word co-occurrence window, and obtained the topic related feature through likelihood ratio, then combined the two features to extract some keywords and score the candidate sentences. The experimental result shows that the proposed methods can capture the main idea of the document set and satisfy the query demand effectively.