Video summarization aims to select a most informative subset of frames in a video to facilitate efficient video browsing. Unsupervised methods usually rely on heuristic training objectives such as diversity and representativeness. However, such methods need to bootstrap the online-generated summaries to compute the objectives for importance score regression. We consider such a pipeline inefficient and seek to directly quantify the frame-level importance with the help of contrastive losses in the representation learning literature. Leveraging the contrastive losses, we propose three metrics featuring a desirable key frame: local dissimilarity, global consistency, and uniqueness. With features pre-trained on an image classification task, the metrics can already yield high-quality importance scores, demonstrating better or competitive performance compared with past heavily-trained methods. We show that by refining the pre-trained features with contrastive learning, the frame-level importance scores can be further improved, and the model can learn from random videos and generalize to test videos with decent performance.