An improved key term weightage algorithm for text summarization using local context information and fuzzy graph sentence score

The process of text summarization is to identify the crux of the document. In the proposed work, summarization is done using three different algorithms. They are sentence based key term weightage, the two way local context information scoring (LCIS) and the fuzzy graph sentence scoring (FGSS). They are used to improve the weight of the key terms, identify LCIS and the centroid of the document by calculating FGSS score respectively. This intelligent system assigning sentence based weightage to the key terms is found to be effective. The present method is domain and language independent. It provides good harmonic mean in comparison with the earlier studies and does not require any training or testing.