Abstract
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.
Highlights
With the tremendous growth of textual information, text summarization helps in finding the essential information in gist form
Query-based text summarization produces detailed answer which will contain necessary infor
In query-based text summarization, we find the summarized text according to query
Summary
With the tremendous growth of textual information, text summarization helps in finding the essential information in gist form. Text summarization contributes in retrieval of important information from a large textual data and reduces the size of the text. It helps in acquiring information in a short period of time. Query-based text summarization is used for retrieving information in shorter form for a specific question. It is different from question answering system. Query-based text summarization produces detailed answer which will contain necessary infor-
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