  We present a domain- and user-preference-agnostic approach to detect
highlightable excerpts from human-centric videos. Our method works on the
graph-based representation of multiple observable human-centric modalities in
the videos, such as poses and faces. We use an autoencoder network equipped
with spatial-temporal graph convolutions to detect human activities and
interactions based on these modalities. We train our network to map the
activity- and interaction-based latent structural representations of the
different modalities to per-frame highlight scores based on the
representativeness of the frames. We use these scores to compute which frames
to highlight and stitch contiguous frames to produce the excerpts. We train our
network on the large-scale AVA-Kinetics action dataset and evaluate it on four
benchmark video highlight datasets: DSH, TVSum, PHD2, and SumMe. We observe a
4-12% improvement in the mean average precision of matching the human-annotated
highlights over state-of-the-art methods in these datasets, without requiring
any user-provided preferences or dataset-specific fine-tuning.
