The objective of this article is to review several automatic question generation systems and find why automatic question generation is still an attraction for researchers. The focus is mainly on the task of question generation, analysis of the approaches and evaluation of various methods of automatic question generation. Pointers for further research are included.Keywords: Automatic question generation, evaluation techniques, quality enhancers, ranking, sentence simplification.A study reveals that an average student asks over 26 questions per hour in one-on-one human tutoring sessions; in contrast, the student poses 120 questions per hour in a learning environment that forces her to ask questions in order to access any information 1 . Conversely, students learn more deeply if prompted by questions 2 . Conventionally, questions are constructed and assessed by tutors. It has been a trend for several decades that automatic question generation (AQG) system generates questions from the corpora using natural language processing.AQG systems were first developed in the 1976 (ref.3). They have been created for English language and vocabulary, medicine, education and using multimedia. The sequence of developments is as follows: learning words [4][5][6] , English 7 , grammar testing 8 , medicine 9 , academic writing 10 , literature review 11 , education 12 (henceforth, Heilman and Smith AQG is abbreviated as HSAQG), multimedia 13 , and finally a recent major development, on-line learning 14 . This article presents a review of more than 50 contributions in the domain of AQG.
Types of questionsIn classroom practice, a tutor evaluates the comprehension of a learner by asking gap-fill type questions (GFQs), multiple choice questions (MCQs), factoid-based questions (FBQs) and deep learning-type questions (DLQs).
Gap-fill questionsA stem is a good question or problem to be solved 15 . To identify a stem and generate a GFQ, an informative sentence is selected from a given document. The selection of information involves identification of semantic features in the entire document.Next, a key phrase or answer phrase (assume it is a noun phrase) is selected; term frequency plays an important role. A distractor (not expected to occur in the question) is a choice given to a learner. A good distractor could be a synonym of the key phrase or an important term in the domain of the key phrase. Distractors in Revup, an AQG, are selected from word2vec, a vector of words 16 . Text summarization features like length of a sentence, number of common tokens, number of noun and pronouns, and position of a sentence are generally considered 17 .
