Application of Transductive Inference SVM Based Relevant Documents Acquiring in Query-Biased Summarization

There is an important issue that text summarization has to embody the personal information need and provide the indicative message for user. In this paper, a method of acquiring relevant documents based on user-feedback information and transductive inference SVM machine learning technology is presented. This method can well avoid subjectivity of deciding relevant documents empirically. To validate the effect, we extract important sentences as the final summary using a feature-fusion sentence selection strategy. The result shows that the method can improve the performance of the query-biased summarization effectively.