Automatic question generation for literature review writing support

This paper presents a novel Automatic Question Generation (AQG) approach that generates trigger questions as a form of support for students\u27 learning through writing. The approach first automatically extracts citations from students\u27 compositions together with key content elements. Next, the citations are classified using a rule-based approach and questions are generated based on a set of templates and the content elements. A pilot study using the Bystander Turing Test investigated differences in writers\u27 perception between questions generated by our AQG system and humans (Human Tutor, Lecturer, or Generic Question). It is found that the human evaluators have moderate difficulties distinguishing questions generated by the proposed system from those produced by human (F-score=0.43). Moreover, further results show that our system significantly outscores Generic Question on overall quality measures. © Springer-Verlag Berlin Heidelberg 2010