An extractive summarization for utilizing learning content using deep learning algorithm: Proposed framework and implementation

Education has always been a critical factor in the long-Term economic development of any society. Most educational institutions use Learning Management Systems (LMSs) to manage and organize students' learning content. These systems contain many learning materials related to a specific topic or course in different formats, such as documents, HTML pages, videos, figures, etc. However, the enormous amount of information in these materials makes it difficult for students to get what they need according to the course objectives. Therefore, summarization techniques could be one way to facilitate the learning process and provide essential content. Therefore, there is a need to summarize the learning content of the course in the guidance of the course outline. Consequently, it is important to investigate how to summarize learning content to enhance and increase students' achievement. This paper proposes a framework for a Guided Extractive Summarization of the Learning Content (GESLC). The main contribution is proposing and developing a novel framework combining several deep learning algorithms to provide efficient summarization techniques to summarize the learning content according to the course outline. Several methods are utilized in this study to evaluate the proposed Framework. As we contribute, the evaluation process shows better results in guiding instructors or students to summarize learning content according to the course objectives to finally have a perfect summary matching the learning process's objectives and enhancing the students' achievement.