Paper Title
Clustered Semantic Graph Approach for Multi-Document Abstractive Summarization

A particular challenge for multi-document summarization is that there is an unavoidable cover in the data put away in various documents. Thus, successful summarization strategies that merge similar data over the documents are desirable. In this work present a system for abstractive summarization of multi-documents, which means to choose contents of summary from semantic representation of the source documents. Contents of the source documents are represented by predicate argument structures by utilizing semantic role labelling. Clustering is performed to eliminate redundancy. To evaluate the proposed semantic graph based approach utilizing ROUGE assessment measures Keywords - Multi-Document Abstractive Summarization; Semantic Role Labelling; Semantic Graph; Semantic Similarity Measure