Challenges in target selection for automatic question generation

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. Automatic Question Generation (AQG) is the technique for generating a right set of questions from a content. A key problem with AQG systems is the selection of the appropriate target sentences and concepts. In this paper, we investigate the difficulties in automatic target selection using unannotated document sources. As the result of the analysis, we identified the following key issues: hard to measure the topic relevance; the large segmentation of the information content and the incomplete background knowledge bases.