Multi-document summarization based on local topics identification and extraction

This paper describes a multi-document summarization method based on local topics identification and extraction. The similarity of sentences is measured by analysis of dependency and semantics. Local topics are found by sentence clustering. The centroid sentence is extracted from each local topic and is ordered to generate summarization. The size of summarization is determined according to content of multiple documents, as a result, the summarization becomes general and concise. Finally, the evaluation and experiment are given, the average precision of summarization and the average ratio of compressibility are 71.4% and 25.2%, respectively.