Determination of Text Summary Using Morphological Filtering of Intuitionistic Fuzzy Hypergraph

Text Summarization has been an area of interest for many years. It refers to creating a concise text of a document without any lose of information. Researchers in the area of natural language processing have developed many abstractive and extractive methods for creating summary. Abstractive summaries modifies the sentences and creates a modified concise form, while extractive summaries pick relevant sentences. The extractive method used in this work is a novel one which models the document as an Intuitionistic Fuzzy Hypergraph (IFHG). The main objectives of the work are to convert a document in to an IFHG, apply morphological operations to it and to create a summary filter. This is the premier work which applies morphological operations on IFHG that is modeled on a text. The method has generated summary which is almost similar to a human generated summary and showed more accuracy when compared with other machine generated summaries. An attempt to apply skelton operation on text hypergraph is also made.