  Despite its wide range of applications, video summarization is still held
back by the scarcity of extensive datasets, largely due to the labor-intensive
and costly nature of frame-level annotations. As a result, existing video
summarization methods are prone to overfitting. To mitigate this challenge, we
propose a novel self-supervised video representation learning method using
knowledge distillation to pre-train a transformer encoder. Our method matches
its semantic video representation, which is constructed with respect to frame
importance scores, to a representation derived from a CNN trained on video
classification. Empirical evaluations on correlation-based metrics, such as
Kendall's $\tau$ and Spearman's $\rho$ demonstrate the superiority of our
approach compared to existing state-of-the-art methods in assigning relative
scores to the input frames.
