In recent years, using video as a learning resource has received a lot of attention and has been successfully applied to many learning activities. In comparison with text-based learning, video learning integrates more multimedia resources, which usually motivate learners more than texts. However, one of the major limitations of video learning is that both instructors and learners must select suitable videos from huge volume of data. The situation is worsened by the fact that video content is usually displayed linearly. In order to solve this problem, we propose a multimedia content summarization and adaptable recommendation framework which is able to extract summaries from raw videos and recommend them to learners automatically. By following the characteristics of multimedia, the generated summary contains both important abstracts and corresponding images, and can be accessed online. Only suitable videos are selected for recommendation based on user profiles. The proposed system is evaluated and compared to text-based learning in terms of ARCS model. The results demonstrate that the proposed video summarization and recommendation framework was not only positive with regard to motivating learners, but also enhanced the video learning experience significantly. Positive results are also found in relation to system usage and satisfaction. (Contains 2 tables and 6 figures.)