Education is a critical factor in the long-term economic development of any society. There are many learning materials that instructors and students use to learn and find knowledge for learning purposes in the academic sector. These learning materials could be in different formats (i.e., PDF files, reports, Case studies, Manuals, etc.). Students and instructors may have difficulty finding information in these materials related to a specific course due to the tremendous amount of information presented. Therefore, this research proposes A Framework for a Guided Extractive Summarization of Learning Content (GESLC) to summarize the course's learning content to achieve the course objectives. The main contribution is proposing and developing a novel framework that combines several deep learning algorithms to provide efficient summarization techniques to summarize the learning content according to the course outline. This Framework consists of four layers. The first layer is the input layer, which will take the course outline and the material content for the selected course. The course outline is analyzed to match the optimal format that describes course objectives and other related information. Second, the primary key is extracted from the outline after identifying the keywords/key phrases. On the other hand, the course materials are preprocessed to be used as input for the summarization algorithm. The summarization layers consist of query-based and deep-learning algorithm layers. First, query-based summarization is applied to produce the candidate sentences according to the keywords extracted from the course outline that match the course's objectives. Finally, the deep learning algorithm – The restricted Boltzmann Machine algorithm-summarizes a candidate's content according to well-defined criteria to produce the final summary. Several methods are utilized in this study to evaluate the proposed Framework. First, statistical evaluation is applied using Recall-Oriented Understudy for Gisting Evaluation (ROUGE), the most popular way to evaluate automatic text summarization. This evaluation calculates three metrics: Precision, Recall, and F Score. The summarized content is considered against the human-based Summarization. The second method evaluation method was human evaluation. The summarized content was given to some experts and evaluated according to some criteria; the third method of evaluation used case studies from an actual educational environment. Two courses are selected from the SQU environment. Both courses contain an adequate number of students registered in one semester. The evaluation process shows better results in guiding instructors and students to summarize learning content according to the course objectives to finally have a perfect summary matching the learning process's objectives and enhancing student achievement. Furthermore, the evaluated methods improve the precision of the state of art for extractive summarization.