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The goal of video summarization is to select keyframes that are visuallydiverse and can represent a whole story of an input video. State-of-the-artapproaches for video summarization have mostly regarded the task as aframe-wise keyframe selection problem by aggregating all frames with equalweight. However, to find informative parts of the video, it is necessary toconsider how all the frames of the video are related to each other. To thisend, we cast video summarization as a graph modeling problem. We proposerecursive graph modeling networks for video summarization, termed SumGraph, torepresent a relation graph, where frames are regarded as nodes and nodes areconnected by semantic relationships among frames. Our networks accomplish thisthrough a recursive approach to refine an initially estimated graph tocorrectly classify each node as a keyframe by reasoning the graphrepresentation via graph convolutional networks. To leverage SumGraph in a morepractical environment, we also present a way to adapt our graph modeling in anunsupervised fashion. With SumGraph, we achieved state-of-the-art performanceon several benchmarks for video summarization in both supervised andunsupervised manners.

SumGraph: Video Summarization via Recursive Graph Modeling