Text clustering using fuzzy rule and lexical term

The fast development of data technology has created a straight system to store and access large quantities of information. The created system has the drawback of extracting potentially valuable knowledge not only in an efficient manner but also in a manner that is easily understood by human beings. One solution to it is in linguistic summarization. This enables the clearing of coherent data summaries that are more consistent with the human cognitive system. The major drawback of the existing applications is in involving high dimensional and distributed info which makes it tough to capture the relevant data. This research focuses on two tasks: one is in selecting the most significant content from source documents and the other is in generating coherent summary by using lexical chaining. In this research paper, an automatic method of text summarization depending on fuzzy sets to extract diversity of structures has been proposed to identify more significant information in the documents. The summary generated by the system is compared to a summary created by domain experts. This method is completely different from other proposed methods described in the literature survey. The text summary is created in the proposed method by testing eight connected features via reduced dimensionality and less fuzzy set rules used for text summarization. In this method, the documents have been summarized by probing connected features and subsequently by different fuzzy sets. The DUC dataset is used during the training and testing phases of the planned summary system. Base Line, weight, accuracy, memory, and F-measure assess the planned system. The outcome of the experiments demonstrations that the proposed technique provides well f-measure than baseline and weight approaches