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Recently, dictionary learning methods for unsupervised video summarization have surpassed traditional video frame clustering approaches. This paper addresses static summarization of videos depicting activities, which possess certain recurrent properties. In this context, a flexible definition of an activity video summary is proposed, as the set of key-frames that can both reconstruct the original, full-length video and simultaneously represent its most salient parts. Both objectives can be jointly optimized across several information modalities. The two criteria are merged into a “salient dictionary” learning task that is proposed as a strict definition of the video summarization problem, encapsulating many existing algorithms. Three specific, novel video summarization methods are derived from this definition: the Numerical, the Greedy and the Genetic Algorithm. In all formulations, the reconstruction term is modeled …