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

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.