Improvement of query-based text summarization using word sense disambiguation

In this paper, a query-based text summarization method is proposed based on common sense knowledge and word sense disambiguation. Common sense knowledge is integrated here by expanding the query terms. It helps in extracting main sentences from text document according to the query. Query-based text summarization finds semantic relatedness score between query and input text document for extracting sentences. The drawback with current methods is that while finding semantic relatedness between input text and query, in general they do not consider the sense of the words present in the input text sentences and the query. However, this particular method can enhance the summary quality as it finds the correct sense of each word of a sentence with respect to the context of the sentence. The correct sense for each word is being used while finding semantic relatedness between input text and query. To remove similar sentences from summary, similarity measure is computed among the selected sentences. Experimental result shows better performance than many baseline systems.