@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Enhancing Human Summaries for Question-Answer Generation in Education
%A Gonzalez, Hannah
%A Dugan, Liam
%A Miltsakaki, Eleni
%A Cui, Zhiqi
%A Ren, Jiaxuan
%A Li, Bryan
%A Upadhyay, Shriyash
%A Ginsberg, Etan
%A Callison-Burch, Chris
%Y Kochmar, Ekaterina
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Madnani, Nitin
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%Y Zesch, Torsten
%S Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F gonzalez-etal-2023-enhancing
%X 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.
%R 10.18653/v1/2023.bea-1.9
%U https://aclanthology.org/2023.bea-1.9
%U https://doi.org/10.18653/v1/2023.bea-1.9
%P 108-118
Markdown (Informal)
[Enhancing Human Summaries for Question-Answer Generation in Education](https://aclanthology.org/2023.bea-1.9) (Gonzalez et al., BEA 2023)
ACL
- Hannah Gonzalez, Liam Dugan, Eleni Miltsakaki, Zhiqi Cui, Jiaxuan Ren, Bryan Li, Shriyash Upadhyay, Etan Ginsberg, and Chris Callison-Burch. 2023. Enhancing Human Summaries for Question-Answer Generation in Education. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 108–118, Toronto, Canada. Association for Computational Linguistics.