Breaking Down Computer Networking Instructional Videos: Automatic Summarization with Video Attributes and Language Models

Authors

Totok Sukardiyono, Department of Electronics and Informatics Engineering Education, Faculty of Engineering, Universitas Negeri Yogyakarta, Indonesia
Muhammad Irfan Luthfi, Department Graduate Institute of Network Learning Technology, National Central University, Taiwan, Province of China
Nisa Dwi Septiyanti, Universitas Muhammadiyah Karanganyar, Indonesia

Keywords

computer networking, instructional video, video attributes, automatic summarization, language model

Document Type

Article

Abstract

Instructional videos have become a popular tool for teaching complex topics in computer networking. However, these videos can often be lengthy and time-consuming, making it difficult for learners to obtain the key information they need. In this study, we propose an approach that leverages automatic summarization and language models to generate concise and informative summaries of instructional videos. To enhance the performance of the summarization algorithm, we also incorporate video attributes that provide contextual information about the video content. Using a dataset of computer networking tutorials, we evaluate the effectiveness of the proposed method and show that it significantly improves the quality of the video summaries generated. Our study highlights the potential of using language models in automatic summarization and suggests that incorporating video attributes can further enhance the performance of these models. These findings have important implications for the development of effective instructional videos in computer networking and can be extended to other domains as well.

First Page

26

Last Page

37

Page Range

26-37

Issue

1

Volume

8

Digital Object Identifier (DOI)

10.21831/elinvo.v8i1.60741

Source

https://journal.uny.ac.id/index.php/elinvo/article/view/60741

Recommended Citation

T. Sukardiyono et al., "Breaking Down Computer Networking Instructional Videos: Automatic Summarization with Video Attributes and Language Models,", vol. 8, no. 1, pp. 26 - 37, Dec 2023.

The definitive version is available at https://doi.org/10.21831/elinvo.v8i1.60741

References

Anwar, M. A., Wahyuni, N. L. S., & Suryana, N. (2021). Sobel Edge Detection in Video Summarization for Teaching Material. Journal of Physics: Conference Series, 1819(1), 012038.

Chan, C. K. K., & Li, K. C. (2018). How long is too long? The effect of video length on learning performance in business and economics-related subjects. Journal of Education for Business, 93(7), 321-328.

Chao, T.-Y., & Chen, W.-H. (2017). Using Instructional Videos to Improve Learning Achievement in Computer Networking. Journal of Educational Technology Development and Exchange, 10(2).

Chao, W. L., Yu, Y. C., Chen, S. L., & Chen, Y. C. (2019). Latent Dirichlet Allocation-based Semantic Topic Extraction for Video Summarization. In 2019 International Conference on Applied System Innovation (ICASI) (pp. 1-4).

Chen, M., Yu, Y., Lu, H., & Zhang, G. (2021). Audio-text summarization for MOOC videos via a unified encoder-decoder framework. IEEE Transactions on Learning Technologies, 14(3), 384-395.

Darmawan, D., & Sulistyo, S. H. (2019, October). Video summarization with text rank and face detection. In 2019 International Conference on Information and Communications Technology (ICOIACT) (pp. 1-4). IEEE.

Feng, X., Ma, F., & Huang, Y. (2020). Deep reinforcement learning-based video summarization. IEEE Transactions on Circuits and Systems for Video Technology, 30(8), 2516-2528.

He, X., Chen, L., Liu, Y., Zhao, Z., & Lu, H. (2020). Generating Text Summaries for Instructional Videos Based on a Language Model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 2467-2478).

Hwang, M., Yoon, J., Kim, D., & Kim, J. (2020). A Survey on Automatic Text Summarization. Journal of Information Science Theory and Practice, 8(1), 1-14.

Kasim, A. M., Pantoja, M. F. F., & Fornillos Jr, R. J. (2021). The Effectiveness of Instructional Videos in Teaching Networking Concepts in a Philippine University. Journal of Information Technology Education: Research, 20, 269-291.

Khamparia, A., Khamparia, S., & Pandey, A. (2019). Automatic text summarization: A review. International Journal of Computer Science and Information Security, 17(6), 77-82.

Kim, H., Lee, S., Park, S., & Lee, G. G. (2021). A review of automatic text summarization with natural language processing. Convergence Information Management Research, 8(2), 97-107.

Kwok, L. F., & Zheng, Y. (2020). Designing an augmented reality app to enhance students' learning experiences: A case study. International Journal of Emerging Technologies in Learning (iJET), 15(5), 132-145.

Lee, J. K., Kim, H. J., Kim, J. H., & Kim, J. W. (2019). The effects of animation-based video on college students' understanding of a complex concept. Journal of Educational Technology & Society, 22(2), 141-153.

Liu, M., Liu, Y., & Ma, Y. (2021). Automatic Video Summarization for E-Learning with Natural Language Processing Technology. IEEE Access, 9, 24563-24572.

Liu, S., Zhang, S., Ma, W., & Li, H. (2021). A pre-training and fine-tuning method for instructional video summarization. arXiv preprint arXiv:2102.10102.

Liu, Y., Huang, Y., Yang, Y., & Zhang, M. (2018). Learning to summarize from scratch. arXiv preprint arXiv:1804.06451.

Li, J., Chen, C., & Li, Y. (2019). Automatic text summarization of academic articles using natural language processing techniques. Information Processing & Management, 56(6), 1689-1702.

Li, J., Wang, X., Yang, Y., Zhang, S., & Sun, M. (2021). Video summarization with pre-trained transformers. arXiv preprint arXiv:2104.01922.

Ma, Z., Peng, H., Hu, Y., Wang, K., & Song, J. (2020). Incorporating pre-trained language models and temporal structure for instructional video summarization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 5909-5919).

Otterbacher, J., Suleman, S., & Lin, J. (2016). Effective video search: summarization with minimal supervised training. International Journal on Digital Libraries, 17(2-3), 107-119.

Peng, H., Wang, H., & Hu, L. (2019). The Application of Instructional Videos in Computer Network Learning. In 2019 4th International Conference on Education and Multimedia Technology (ICEMT) (pp. 1-5). IEEE.

Salomon, J. (2017). Using instructional video in a classroom context: Effects on engagement and retention. Journal of Information Technology Education: Research, 16, 335-347.

Wang, Q., Zhang, Y., & Song, Y. (2016). Video summarization based on feature clustering and multiple-instance learning. Neurocomputing, 205, 358-368.

Wang, T., Zhao, Y., & Huang, C. (2020). An approach to generating instructional videos using natural language processing. Journal of Educational Technology Development and Exchange (JETDE), 13(1), 1-16.

Wang, W., & Chen, W. (2018). Evaluating the Effectiveness of Using Online Video Instruction for Practical Skill Training in Computer Networks. In 2018 IEEE 4th International Conference on Computer and Communications (ICCC) (pp. 594-598). IEEE.

Wang, Y., Li, J., Yang, Y., & Sun, M. (2021). Multi-modal video summarization with transformer. arXiv preprint arXiv:2104.03544.

Wang, Y., Yang, Y., Li, J., & Sun, M. (2020). Video summarization with attention-based encoder-decoder networks. arXiv preprint arXiv:2004.14098.

Yang, M., Zhang, L., Feng, F., & Gao, W. (2019). Automatic video summarization using natural language processing. IEEE Transactions on Multimedia, 21(11), 2979-2991.

Zhai, W., Yang, X., Zhang, J., & Chen, Y. (2022). A Survey on Automatic Text Summarization. ACM Computing Surveys, 55(1), 1-33.

Zhang, D., Zhou, L., Briggs, R. O., & Nunamaker Jr, J. F. (2019). Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Information & Management, 56(1), 103-119.

Zhang, H., Li, Y., Zhang, S., & Huang, Z. (2020). Video summarization via attention-based LSTM with joint learning. IEEE Transactions on Circuits and Systems for Video Technology, 31(4), 1334-1344.