Abstract
A large amount of information is available on the Internet, however sometimes it is required to obtain small amount of information in shorter period of time. For this, systems make use of text summarization which is an application of Natural Language Processing. Text summarization is the process of shortening of data computationally, to generate a summary that will contain relevant information and salient features from original text. In this paper, a text summarization technique is proposed to acquire information about famous Indian historical monuments. Using the proposed model, users are able to get information about Indian historical monuments in the form of a summary. The proposed system works as a query-based text summarizer which uses latent semantic analysis and vector space model in order to search answers to the basic queries about the monument. The data which is to be summarized is generated by the algorithm itself using web scraping. As a performance parameter ROUGE score is used. The proposed approach is tested and validated for various Indian historical monuments and satisfactory results are obtained.
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