10. A non-transitory computer-readable medium having encoded therein programming code executable by a processor to perform operations comprising: accessing learning courses from an open educational resource (OER) server, the learning courses including learning materials for the learning courses and including learning courses metadata that is associated with the learning courses and the learning materials; accessing lecture video metadata associated with a lecture video from an educational server; accessing a lecture video transcript that includes a transcribed text of the lecture video, the transcribed text being correlated to a time in which verbal recitation of the transcribed text occurred in the lecture video; discovering one or more candidate learning courses of the accessed learning courses, the candidate learning courses being related to the lecture video and being based on a measured similarity between the lecture video metadata and the learning courses metadata; extracting key phrases from the learning materials of the discovered candidate learning courses; assigning weights to the extracted key phrases based on a position of the extracted key phrases in the learning materials of the discovered candidate learning courses, frequencies with which the extracted key phrases appear in the learning materials of the discovered candidate learning courses, and a type of the discovered candidate learning course in which the key phrases appear; generating two or more topic segments in the lecture video transcript using sliding-window segmentation, wherein: the sliding-window segmentation includes construction of a refined vector representation of windows created in the lecture video transcript, each of the generated topic segments includes two topic segment borders and a portion of the lecture video transcript between the two topic segment borders, and the topic segment borders of each of the generated topic segments are adjusted based on presence of the extracted key phrases and the assigned weights of the extracted key phrases within the portion of the lecture video transcript between the topic segment borders of each of the generated topic segments; and apportioning the lecture video into two or more topic-specific portions based on the two or more topic segments generated in the lecture video transcript and time correlation between the lecture video transcript and the lecture video.
12. The non-transitory computer-readable medium of claim 10 , wherein the extracting includes: accessing the learning materials of the discovered candidate learning courses; detecting positions of phrases in the learning materials based on a text format analysis; extracting repeated phrases using a generalized suffix tree; in response to at least one of: the phrases appearing in a syllabus or a lecture note title, the phrases appearing in a section title or a page title, or the phrases appearing in the learning materials above a particular frequency threshold, designating the phrases as key phrases; processing stop words in the key phrases; and extracting and unifying abbreviations from the key phrases using heuristic rules.