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Face to an ever growing video collection, user are in a quest for a technology to effectively browse videos in a short time without missing important content. In this paper, we present a new approach to extract semantic video information from a soccer video and create personalized summaries. Our approach builds upon segmentation and indexation steps that rely on both low-level (graphic) and text-based treatment of the soccer video. The video summarization operates in three steps: identification of pertinent segments to appear in the summary, identification of the ratio of participation of each pertinent segment, and identification of frames to participate in the summary. We present different methods for summarization along with their experimental evaluations.