Query-Based Extractive Text Summarization Using Sense-Oriented Semantic Relatedness Measure

Abstract

This paper presents a query-based extractive text summarization method by using sense-oriented semantic relatedness measure. We have proposed a Word Sense Disambiguation (WSD) technique to find the exact sense of a word present in the sentence. It helps in extracting query relevance sentences while calculating the sense-oriented sentence semantic relatedness score between the query and input text sentence. The proposed method uses five unique features to make clusters of query-relevant sentences. A redundancy removal technique is also put forward to eliminate redundant sentences. We have evaluated our proposed WSD technique with other existing methods by using Senseval and SemEval datasets. Experimental evaluation and discussion signifies the better performance of proposed WSD method over current systems in terms of F-score. We compare our proposed query-based extractive text summarization method with other methods participated in Document Understanding Conference (DUC) and as well as with current methods. Evaluation and comparison state that the proposed query-based extractive