Abstract: Automatic multi-document summarization is an outgrowth of single document summarization. A statistical approach to multi-document summarization is presented. It utilizes the semantic relevance between segments of documents. Text-tiling algorithm is implemented to break documents into semantic relevant segments. These segments are merged into some topic classes according to the semantic similarity by using clustering algorithm. The representative segments are extracted from topic classes to form the summarization result. By using real Chinese corpus, experimental results show the system’ s effectiveness and suitability.