Text Summarization is the method by which the noteworthy segments of a text are recovered. Diverse philosophies are created till now relying on a few parameters to discover the summary based on the position, configuration and sort of the sentences in an information content, organizations of various words, recurrence of a specific word in content and so on. As indicated by various information sources, these predefined limitations enormously influence the outcome. The proposed approach generates the outline by undertaking an unsupervised learning method. The significance of a sentence in an information content is assessed by the assistance of Simplified Lesk Algorithm. As an online semantic dictionary WordNet is utilized. To start with, this approach assesses the weights of the considerable number of sentences of a content independently utilizing the Simplified Lesk Algorithm's calculation and organizes them in diminishing request as indicated by their weights. Next, depending upon the given level of the synopsis, a specific number of sentences are chosen from that requested rundown. The proposed approach gives best outcomes up to half outline of the original content.