Abstract
Modern educational technology systems allow learners to access large amounts of learning materials such as educational videos, learning notes, and teaching books. Automated summarization techniques simplify the access and exploration of complex data collections by producing synthetic versions of the original content. This paper addresses the problem of video lecture summarization by means of abstractive techniques. To enhance the accessibility of the video lecture content in challenging contexts or while coping with learners with special needs it produces a synthetic textual summary condensing the key concepts mentioned in the lecture’s speech. Unlike prior works based on extractive methods, the proposed method can produce more readable and actionable summaries, not necessarily composed of existing portions of speech content. To compensate the lack of annotated data, it also opportunistically reuses the pretrained models available for meeting summarization. The experimental results achieved on a benchmark dataset show that the proposed method generates more fluent and actionable summaries than prior approaches simply relying on content extraction. Finally, we also envision further applications of summarization techniques to learning content. The future prospects of use of summarization techniques in education have shown to go well beyond video summarization.
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Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
The datasets analyzed during and/or analysed during the current study are available at https://ocw.mit.edu/
Notes
https://ocw.mit.edu/about/ Latest access: August 2022
http://uis.unesco.org/en/topic/international-standard-classification-education-isced Latest access: May 2022
https://cloud.google.com/speech-to-text Latest access: May 2022
https://pypi.org/project/fastpunct/ Latest access: May 2022
https://pypi.org/project/pytextrank/ Latest access: June 2022
https://pypi.org/project/sentence-transformers/0.3.0/ Latest access: June 2022
https://huggingface.co/docs/transformers/ Latest access: September 2022
https://ocw.mit.edu/about/ Latest access: August 2022
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The paper is an extended version of the preliminary work presented in Benedetto et al. (2022). Unlike the prior work, the current manuscript contains \(\bullet \) An overview of the existing benchmark datasets for video lecture summarization (see Section 2.1 of the current manuscript). \(\bullet \) A more thorough description of the presented methodology (see Section 2.2). \(\bullet \) A validation of the summaries generated from the open-source video lectures available in the MIT OpenCourseWare repository (see Sections 2.3, 2.4, and 2.5). \(\bullet \) A more extensive overview of the related works on summarization in education (See Section 3). \(\bullet \) A discussion of the future prospects of use of summarization techniques in education (See Section 4).
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Benedetto, I., La Quatra, M., Cagliero, L. et al. Abstractive video lecture summarization: applications and future prospects. Educ Inf Technol 29, 2951–2971 (2024). https://doi.org/10.1007/s10639-023-11855-w
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DOI: https://doi.org/10.1007/s10639-023-11855-w