The recent progress of digital media has stimulated the creation, storage and distribution of data, such as digital videos, generating a large volume of data and requiring efficient technologies to increase the usability of these data. Video summarization methods generate concise summaries of video contents and enable faster browsing, indexing and accessing of large video collections, however, these methods often perform slow with large duration and high quality video data. One way to reduce this long time of execution is to develop a parallel algorithm, using the advantages of the recent computer architectures that allow high parallelism. This paper introduces parallelizations of a summarization method called VSUMM, targetting either Graphic Processor Units (GPUs) or multicore Central Processor Units (CPUs), and ultimately a sensible distribution of computation steps onto both hardware to maximise performance, called "hybrid". We performed experiments using 180 videos varying frame resolution (320 × 240, 640 × 360, and 1920 × 1080) and video length (1, 3, 5, 10, 20, and 30 minutes). From the results, we observed that the hybrid version reached the best results in terms of execution time, achieving 7× speed up in average. © The Authors. Published by Elsevier B.V.