Automatic Question Generation: A Systematic Review

Abstract

Today's educational systems need an efficient tool to perform competently assessment of students on their major concepts they learnt from study material. Preparing a set of questions for assessment can be time consuming for teachers while getting questions from external sources like assessment books or question bank might not be relevant to content studied by students. Automatic Question Generation (AQG) is the technique for generating a right set of questions from a content, which can be text. Automatic question generation (QG) is a very important yet challenging problem in NLP. It is defined as the task of generating syntactically sound, semantically correct and relevant questions from several input formats like text, a structured database or a knowledge base. Question generation can be naturally applied in many domains such as MOOC, automated help systems, search engines, chatbot systems (e.g. for customer interaction), and healthcare for analyzing mental health. AQG has the got the immense attention from researchers in a field of computational linguistics. The review paper focuses on the recants on-going research on NLP for generating automatic questions from the text through various methods.

Keywords: Automatic Question Generation, Natural Language Processing, Natural Language Generator, Natural Language Understanding