  We summarize full-length movies by creating shorter videos containing their
most informative scenes. We explore the hypothesis that a summary can be
created by assembling scenes which are turning points (TPs), i.e., key events
in a movie that describe its storyline. We propose a model that identifies TP
scenes by building a sparse movie graph that represents relations between
scenes and is constructed using multimodal information. According to human
judges, the summaries created by our approach are more informative and
complete, and receive higher ratings, than the outputs of sequence-based models
and general-purpose summarization algorithms. The induced graphs are
interpretable, displaying different topology for different movie genres.
