Automatic Text categorization and summarization using rule reduction

Text mining is a new field that attempts to bring together meaningful information from natural language text. Automatic Text categorization and summarization is the process of assigning pre-defined class labels to incoming, unclassified documents. The class labels are defined based on a set of examples of pre-classified documents used as a training corpus. This research work comprises an automatic text categorization and summarization approach to analyze the structure of input text. In this work a text analyzer is developed to derive the structure of the input text using rule reduction technique in three stages namely, Token Creation, Feature Identification and Categorization and Summarization. This analyzer is tested with sample input texts and gives noteworthy results. Extensive experimentation validates the selection of parameters and the efficacy of our approach for text classification. This work can be expanded and used in many practical applications, including indexing for document retrieval, organizing and maintaining large catalogues of Web resources, automatically extracting metadata, and Word sense disambiguation, etc.