Video is an increasingly important resource in higher education. A key limitation of lecture video is that it is fundamentally a sequential information stream. Quickly accessing the content aligned with specific learning objectives in a video recording of a classroom lecture is challenging. Recent research has enabled automatic reorganization of a lecture video into segments discussing different subtopics. This paper explores AI generation of visual and textual summaries of lecture video segments to improve navigation. A visual summary consists of a subset of images in the video segment that are considered the most unique and important by image analysis. A textual summary consists of a set of keywords selected from the screen text in the video segment by analyzing several factors including frequency, font size, time on screen, and existence in domain and language dictionaries. Evaluation was performed against keywords and summary images selected by human experts with the following results for the most relevant formulations. AI driven keyword selection yielded an F-1 score of 0.63 versus 0.26 for keywords sampled randomly from valid keyword candidates. AI driven visual summary yielded an F-1 score of 0.70 versus 0.59 for K-medoid clustering that is often employed for similar tasks. Surveys showed that 79% (72%) of the users agreed that a visual (textual) summary made a lecture video more useful. This framework is implemented in Videopoints, a real-world lecture video portal available to educational institutions.