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Movie trailers perform multiple functions: they introduce viewers to thestory, convey the mood and artistic style of the film, and encourage audiencesto see the movie. These diverse functions make trailer creation a challengingendeavor. In this work, we focus on finding trailer moments in a movie, i.e.,shots that could be potentially included in a trailer. We decompose this taskinto two subtasks: narrative structure identification and sentiment prediction.We model movies as graphs, where nodes are shots and edges denote semanticrelations between them. We learn these relations using joint contrastivetraining which distills rich textual information (e.g., characters, actions,situations) from screenplays. An unsupervised algorithm then traverses thegraph and selects trailer moments from the movie that human judges prefer toones selected by competitive supervised approaches. A main advantage of ouralgorithm is that it uses interpretable criteria, which allows us to deploy itin an interactive tool for trailer creation with a human in the loop. Our toolallows users to select trailer shots in under 30 minutes that are superior tofully automatic methods and comparable to (exclusive) manual selection byexperts.

Finding the Right Moment: Human-Assisted Trailer Creation via Task Composition