Enrich Framework for Multi-document Summarization Using Text Features and Fuzzy Logic

The rapid growth of Information Technology triggers collection of documents in massive form, so to find the important information from multiple document is a complex task. The multiple documents summarization is task of producing assured summary from these document set. There are other summarization techniques like sentence clustering, term weight etc. However, these techniques use only two or three feature of text to find the importance of considered sentence. In this paper, we put forward an idea of text summarization which considers multiple extracted features by applying natural language processing (NLP) protocol. The ten feature of text are extracted and these feature classified on the basis of fuzzy logic to get the best documents summary. The key features are preprocessing, feature scoring, inference engine, and fuzzy logic.