Automatic text summarization based on sentences clustering and extraction

Technology of automatic text summarization plays an important role in information retrieval and text classification, and may provide a solution to the information overload problem. Text summarization is a process of reducing the size of a text while preserving its information content. This paper proposes a sentences clustering based summarization approach. The proposed approach consists of three steps: first clusters the sentences based on the semantic distance among sentences in the document, and then on each cluster calculates the accumulative sentence similarity based on the multi-features combination method, at last chooses the topic sentences by some extraction rules. The purpose of present paper is to show that summarization result is not only depends the sentence features, but also depends on the sentence similarity measure. The experimental result on the DUC 2003 dataset show that our proposed approach can improve the performance compared to other summarization methods.