Text summarization is basically summarizing of the given paragraph with the use of natural language processing and machine learning. There has been an explosion in the quantity of textual content records from lot of sources. This quantity of textual content is a useful supply of facts and information which needs to be efficiently summarized to be useful. In this paper, the primary tactics to computerized textual content summarization were described. The distinctive approaches for summarization and the effectiveness and shortcomings of the distinctive methods were described. The machine works through assigning rankings to sentences withinside the document to be summarized, and the use of the maximum scoring sentences in the summary. Score values are primarily based totally on functions extracted from the sentence. A linear mixture of function rankings was used. Almost all the mappings from function to score and the coefficient values withinside the linear mixture were derived from a training corpus. Some anaphor decision was performed. In addition to primary summarization, a strive was made to address the issue of targeting the text at the user. The meant user was taken into consideration to have little history information or analyzing ability. The machine enabled through simplifying the individual words or phrases used in the summary and through drawing the pre-needful history facts from the web.