How to Engage your Readers? Generating Guiding Questions to Promote Active Reading
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Date
2024-08
Publication Type
Conference Paper
ETH Bibliography
yes
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OPEN ACCESS
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Abstract
Using questions in written text is an effective strategy to enhance readability. However, what makes an active reading question good, what the linguistic role of these questions is, and what is their impact on human reading remains understudied. We introduce GuidingQ, a dataset of 10K in-text questions from textbooks and scientific articles. By analyzing the dataset, we present a comprehensive understanding of the use, distribution, and linguistic characteristics of these questions. Then, we explore various approaches to generate such questions using language models. Our results highlight the importance of capturing inter-question relationships and the challenge of question position identification in generating these questions. Finally, we conduct a human study to understand the implication of such questions on reading comprehension. We find that the generated questions are of high quality and are almost as effective as human-written questions in terms of improving readers’ memorization and comprehension.
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Publication status
published
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Book title
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Journal / series
Volume
Pages / Article No.
11749 - 11765
Publisher
Association for Computational Linguistics
Event
62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09684 - Sachan, Mrinmaya / Sachan, Mrinmaya
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Related publications and datasets
Is supplemented by: https://github.com/eth-lre/engage-your-readers