18. The at least one machine readable medium of claim 17 , wherein generating the semantic model includes extracting features of the frames.
17. At least one non-transitory computer-readable medium including instructions that, when executed by a machine, cause the machine to perform operations for automatic video summarization, the operations comprising: obtaining a video; generating a semantic model of the video from frames of the video; assigning respective relevancy scores to the frames; initializing the semantic model with the respective relevancy scores including constructing a graph in which nodes correspond, to the frames, node values correspond to respective relevancy scores of the frames, and edges are weighted by the inverse distance between frames established in the semantic model; and iteratively processing the semantic model to produce a set of sub-scenes, each iteration comprising: conversing die semantic model following initialization; selecting a sequence of frames with a highest relevancy score after converging; and re-initializing the semantic model by fixing the relevancy scores for the selected sequence of frames.