Extractive Approach For Query Based Text Summarization

The last few years have seen a tremendous surge in the information that is being dumped online. In this digital world every organization have their respective website which gives a detailed knowledge about them to the public. Considering organizations like educational institutions, their official websites provide all the necessary information from admissions to research works carried out. Prospective students, parents, researchers and academicians refer the website to in order to get relevant answers to their query. In order to retrieve an answer for their query one to has to spend a lot of time searching through the website, reading through several subpages available and consolidating the relevant information. This paper presents a model to retrieve concise and irredundant answers to various questions / queries regarding an educational institution -Amrita School of Engineering. Our model uses various Natural Language Processing (NLP) based techniques for text summarization to give appropriate results. Hybrid similarity measure and clustering algorithm are used for retrieving relevant data and removing redundancy respectively. Our model was tested by many users and the results were accurate in 86% of the cases.