  We propose a graph-based representation learning framework for video
summarization. First, we convert an input video to a graph where nodes
correspond to each of the video frames. Then, we impose sparsity on the graph
by connecting only those pairs of nodes that are within a specified temporal
distance. We then formulate the video summarization task as a binary node
classification problem, precisely classifying video frames whether they should
belong to the output summary video. A graph constructed this way aims to
capture long-range interactions among video frames, and the sparsity ensures
the model trains without hitting the memory and compute bottleneck. Experiments
on two datasets(SumMe and TVSum) demonstrate the effectiveness of the proposed
nimble model compared to existing state-of-the-art summarization approaches
while being one order of magnitude more efficient in compute time and memory
