Text summarization is an important research topic in the field of information technology because of the large volume of texts, and the large amount of data found on the Internet and social media. The task of summarizing the text has gained great importance that requires finding highly efficient ways in the process of extracting knowledge in various fields, Thus, there was a need for methods of summarizing texts for one document or multiple documents. The summarization methods aim to obtain the main content of the set of documents at the same time to reduce redundant information. In this paper, an efficient method to summarize texts is proposed that depends on the word association algorithm to separate and merge sentences after summarizing them. As well as the use of data mining technology in the process of redistributing information according to the (K-Mean) algorithm and the use of (Term Frequency Inverse Document Frequency TF-IDF) technology for measuring the properties of summarized texts. The experimental results found that the summarization ratios are good by deleting unimportant words. Also, the method of extracting characteristics for texts was useful in grouping similar texts into clusters, which makes this method possible to be combined with other methods in artificial intelligence such as fuzzy logic or evolutionary algorithms in increasing summarization rates and accelerating cluster operations.