  Video summarization aims at choosing parts of a video that narrate a story as
close as possible to the original one. Most of the existing video summarization
approaches focus on hand-crafted labels. As the number of videos grows
exponentially, there emerges an increasing need for methods that can learn
meaningful summarizations without labeled annotations. In this paper, we aim to
maximally exploit unsupervised video summarization while concentrating the
supervision to a few, personalized labels as an add-on. To do so, we formulate
the key requirements for the informative video summarization. Then, we propose
contrastive learning as the answer to both questions. To further boost
Contrastive video Summarization (CSUM), we propose to contrast top-k features
instead of a mean video feature as employed by the existing method, which we
implement with a differentiable top-k feature selector. Our experiments on
several benchmarks demonstrate, that our approach allows for meaningful and
diverse summaries when no labeled data is provided.
