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 text summarization method outperforms many existing methods. As an unsupervised learning algorithm, we obtained highest ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score for all three DUC 2005, 2006 and 2007 datasets. Our proposed method is also quite comparable with other supervised learning based algorithms. We also observe that our query-based extractive text summarization method can recognize query relevance sentences which meet the query need.