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A tool to provide an idea of the content of a given video is becoming a need in the current Web scenario, where the presence of videos is increasing day after day. Dynamic summarization techniques can be used to this aim as they set up a video abstract, by selecting and sequencing short video clips extracted from the original video. Needless to say, the selection process is critical. In this paper we focus our attention on clustering algorithms to provide such selection and we investigate the effects of their employment in the Web scenario. Clustering algorithms are very effective in producing static video summary, but few works consider them for video abstract production. For this reason, we set up an experimental scenario where we investigate their performance considering different categories of video, different abstract lengths and different low-level video analysis. Results show that clustering techniques can be useful only for some categories of videos and only if the selection process is based on video scene characteristics. Furthermore, the investigation also shows that to provide a customized service (user can freely decide the abstract time length), only fast clustering algorithm should be used.