Concept Frequency: A Feature Set Based Text Compression Model

A summary is a shorter version of the original. Such a simplification highlights the major points from the much longer subject, such as a text, speech, film, or event. The purpose is to help the audience get the gist in a short period of time. Automatic summarization involves reducing a text document or a larger corpus of multiple documents into a short set of words or paragraph that conveys the main meaning of the text. In this paper, we propose a new multi-document summarization approach that makes use of a feature called ‘concept frequency’ that verifies sentences nearness with a cluster opted. Hence the summarization relevance would be more effective. A novel feature called exemplar is to help simultaneously deals with sentence ranking. A fuzzy medoid-based clustering approach is used to produce sentence clusters or subsets where each of them corresponds to a subtopic of the related topic. Keywords— Concept frequency, exemplar, text Summarization