Abstract
Prior work in automatic question generation typically creates questions from sentences in a text. In contrast, the work presented here creates questions from a text passage in a holistic approach to natural language understanding and generation. Several NLP techniques including topic modeling are combined in an ensemble approach to identify important concepts, which then are used to create questions. Evaluation of the generated questions revealed that they are of high linguistic quality and are also important, conceptual questions, compared to questions generated by sentence-level question generation systems.
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