Paper Title
Objective-Type Question Paper Generation

Abstract
In the modern day, a major portion of education has become digital. From classes to exams, even the study material is now available digitally. In this paper, we aim to deal with the digital study material and selectively transform the text-based data to automate the creation of an assessment item with the use of powerful NLP libraries like NLTK and Spacy to perform tasks like text summarization and named entity recognition, and fine-tune T5 Transformer to generate questions from sentences. We will focus on generation of objective type questions [questions that have only one potentially correct answer]. Our goal is to use some of the very commonly used text supported file formats like PDF and ePUB as the input and procedurally generate objective type question papers in the .PDF format. These question papers can be used by anyone to test their own knowledge on the subject material provided or they can also be used by teachers to further provide it to their students or use it to create their own set of questions from the question bank instead of generating them manually from scratch. Keywords - Digital, Text-Based Data, NLTK, PDF, ePUB, Automate