@inproceedings{gonzalez-etal-2023-enhancing,
    title = "Enhancing Human Summaries for Question-Answer Generation in Education",
    author = "Gonzalez, Hannah  and
      Dugan, Liam  and
      Miltsakaki, Eleni  and
      Cui, Zhiqi  and
      Ren, Jiaxuan  and
      Li, Bryan  and
      Upadhyay, Shriyash  and
      Ginsberg, Etan  and
      Callison-Burch, Chris",
    editor = {Kochmar, Ekaterina  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Madnani, Nitin  and
      Tack, Ana{\"\i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng  and
      Zesch, Torsten},
    booktitle = "Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bea-1.9",
    doi = "10.18653/v1/2023.bea-1.9",
    pages = "108--118",
    abstract = "We address the problem of generating high-quality question-answer pairs for educational materials. Previous work on this problem showed that using summaries as input improves the quality of question generation (QG) over original textbook text and that human-written summaries result in higher quality QG than automatic summaries. In this paper, a) we show that advances in Large Language Models (LLMs) are not yet sufficient to generate quality summaries for QG and b) we introduce a new methodology for enhancing bullet point student notes into fully fledged summaries and find that our methodology yields higher quality QG. We conducted a large-scale human annotation study of generated question-answer pairs for the evaluation of our methodology. In order to aid in future research, we release a new dataset of 9.2K human annotations of generated questions.",
}

We address the problem of generating high-quality question-answer pairs for educational materials. Previous work on this problem showed that using summaries as input improves the quality of question generation (QG) over original textbook text and that human-written summaries result in higher quality QG than automatic summaries. In this paper, a) we show that advances in Large Language Models (LLMs) are not yet sufficient to generate quality summaries for QG and b) we introduce a new methodology for enhancing bullet point student notes into fully fledged summaries and find that our methodology yields higher quality QG. We conducted a large-scale human annotation study of generated question-answer pairs for the evaluation of our methodology. In order to aid in future research, we release a new dataset of 9.2K human annotations of generated questions.
Gonzalez, Hannah Dugan, Liam Miltsakaki, Eleni Cui, Zhiqi Ren, Jiaxuan Li, Bryan Upadhyay, Shriyash Ginsberg, Etan Callison-Burch, Chris
Enhancing Human Summaries for Question-Answer Generation in Education