An Effective Approach for Abstractive Text Summarization using Semantic Graph Model

Abstract

In the last two decades, exemplary advancements are being happened in the field of hidden Knowledge Extraction (KE) from huge amount of data. Natural Language Processing (NLP) is a predominant research area which are practicing in the textual analytics. Text summarization is the process which uses NLP, to extract the significant sentences from the multiple original documents without affecting its original meaning. This research work aims to provide an Abstractive Text Summarization for multi-documents using semantic graph based approach. Creating abstract from the multiple documents are the most difficult task. The abstract created by keeping important phrases taken from big data should not give up its authenticity. To keep the sentence coherent, this research work has proposed a semantic graph. The semantic graph (relationship between the sentences) are weighed using the graph ranking methodology. A ranking algorithm is proposed using Pearson's correlation coefficient to find the important sentences. ROUGE scores are evaluated for the proposed work and compared with the existing TextRank algorithm.