Abstract
— The aim of question answering is to respond to queries that are expressed in natural language (QA). Question answering systems offer automated ways to locate solutions to queries posed in natural language. One important development in information can be seen in the concept of question-answering systems particularly in its capacity to retrieve information resources in a natural way, retrieval technologies method through effective wordfor-word querying and retrieval of the appropriate responses. The development of machine learning algorithms like text summarizing, which can automatically shorten lengthier texts and extract summaries of individual sections of text without losing the meaning, is another result of advances in NLP. Text summary is a cutting-edge method of information processing in a time-constrained and efficiency-obsessed culture. It shortens the amount of time needed to read and makes finding, analyzing, and assimilation of relevant information simpler. In order to answer the questions that users have asked and to provide a concise overview of the same context, a novel method for extracting text from a picture and converting it into text files has been developed. In order to validate the proposed methodologies, a manual evaluation of the quality of the responses was also carried out.
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