An Approach for generating best possible questions from the given text using Natural Language Processing

Abstract

A crucial ability for every person is the capacity to ask pertinent questions. By automating the process of question formation, an automatic question generator is able to decrease the time and effort needed for manual question creation. Along with benefitting educational institutions like schools and colleges, automated question generation can be used in chatbots and for automated tutoring systems. Question Generation is an area in NLP that is still under research for greater accuracy. Research work has been done in many languages too. The goal of an automatic question generator is to generate syntactically and semantically correct questions, valid according to the given input. The Bidirectional Encoder Representations from Transformers (BERT) model is one of the pre-trained models adopted to implement the same. Additionally, we used Python packages, including NLTK, Spacy, and PKE. To test our findings, we evaluated the validity and relevance of generated questions using human-level cognition and evaluation. We were successful in creating inquiries that adequately reflected several of the peculiarities of English so that a person might comprehend them.