Video summarization has received widely attention as a technique to quickly display the main video content.Existing graph model based method takes video frames as vertices and uses the edges to build the relationship between two vertices

which may not well capture the complex relationship among the video frames.To overcome this drawback

we present a novel method based on hyper-graph ranking to generate a static video summarization

and name it Hyper-Graph Ranking based Video Summarization (HGRVS).Specifically

HGRVS first builds a video hyper-graph model to connect the video frames that have internal relations with hyper-edges;then classifies the video frames with an idea borrowed from the hyper-graph ranking method to provide the candidate keyframes;the video summarization is finally determined with the keyframes chosen by an objective function.Extensive subjective and objective experiments on the popular Open Video Project and YouTube datasets clearly demonstrate the superiority of HGRVS to the state-of-the-art approaches.