Indian Journal of Science and Technology

Abstract

Objectives: Themain objective of automatic text summarization is to compress the document into a smaller version by preserving the important concepts. Methods/Statistical Analysis:This work proposes a hybrid approach of Singular Value Decomposition and Named Entity Recognition to extract important sentences present in a document. The extracted sentences are used to create a probabilistic graphical model calledaBeliefcNetwork. This graph model represents documentsummary in concept level. We have used a modified Page Rank algorithm to find the most ranked noun phrase. From this noun phrase we extracted the most relevant sentences. Findings: Our abstractive graph based model for a document generates novel sentences as it uses the concept of triplets (Subject, Verb, and Object). It identifies whether a sentence is created by structural rearrangement of another sentence.Using SVD(Singualr Value Decomposition) and NER(Named Entity Recognition) we extracted relevant information present in a document so that entire document is crushed in to a graph model. We can use this model for documents similarity as well as for plagiarism detection. Application/Improvements.Experimental results of our proposed system show that use of named entities and SVD increases the accuracy of summarizer.
Keywords: Abstractive Text Summarization, Named Entity Recognition, Page Ranking Algorithm, Singular Value Decomposition, Text Documents