%0 Conference Proceedings %T Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization %A Zhao, Zhenjie %A Hou, Yufang %A Wang, Dakuo %A Yu, Mo %A Liu, Chengzhong %A Ma, Xiaojuan %Y Muresan, Smaranda %Y Nakov, Preslav %Y Villavicencio, Aline %S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) %D 2022 %8 May %I Association for Computational Linguistics %C Dublin, Ireland %F zhao-etal-2022-educational %X Generating educational questions of fairytales or storybooks is vital for improving children’s literacy ability. However, it is challenging to generate questions that capture the interesting aspects of a fairytale story with educational meaningfulness. In this paper, we propose a novel question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. To train the event-centric summarizer, we finetune a pre-trained transformer-based sequence-to-sequence model using silver samples composed by educational question-answer pairs. On a newly proposed educational question-answering dataset FairytaleQA, we show good performance of our method on both automatic and human evaluation metrics. Our work indicates the necessity of decomposing question type distribution learning and event-centric summary generation for educational question generation. %R 10.18653/v1/2022.acl-long.348 %U https://aclanthology.org/2022.acl-long.348 %U https://doi.org/10.18653/v1/2022.acl-long.348 %P 5073-5085