  In this work we propose a novel method for supervised, keyshots based video
summarization by applying a conceptually simple and computationally efficient
soft, self-attention mechanism. Current state of the art methods leverage
bi-directional recurrent networks such as BiLSTM combined with attention. These
networks are complex to implement and computationally demanding compared to
fully connected networks. To that end we propose a simple, self-attention based
network for video summarization which performs the entire sequence to sequence
transformation in a single feed forward pass and single backward pass during
training. Our method sets a new state of the art results on two benchmarks
TvSum and SumMe, commonly used in this domain.
