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Automatic text summarization is to compress an original document into an abridged version by extracting almost all of the essential concepts with text mining techniques. This research focuses on developing a hybrid automatic text summarization approach, KCS, to enhancing the quality of summaries. KCS employs the K-mixture probabilistic model to establish term weights in a statistical sense, and further identifies the term relationships to derive the connective strength (CS) of nouns. Sentences are ranked and extracted based on their CS values. We conduct two experiments to justify the proposed approach. The quality of extracted summary is examined by its capability of increasing text classification accuracy. The results show that our proposed approach, KCS, performs best among all approaches considered. It implies that KCS can extract more representative sentences from the document and its feasibility in text summarization applications is thus justified.