  Video content creation keeps growing at an incredible pace; yet, creating
engaging stories remains challenging and requires non-trivial video editing
expertise. Many video editing components are astonishingly hard to automate
primarily due to the lack of raw video materials. This paper focuses on a new
task for computational video editing, namely the task of raking cut
plausibility. Our key idea is to leverage content that has already been edited
to learn fine-grained audiovisual patterns that trigger cuts. To do this, we
first collected a data source of more than 10K videos, from which we extract
more than 255K cuts. We devise a model that learns to discriminate between real
and artificial cuts via contrastive learning. We set up a new task and a set of
baselines to benchmark video cut generation. We observe that our proposed model
outperforms the baselines by large margins. To demonstrate our model in
real-world applications, we conduct human studies in a collection of unedited
videos. The results show that our model does a better job at cutting than
random and alternative baselines.
