  According to screenwriting theory, turning points (e.g., change of plans,
major setback, climax) are crucial narrative moments within a screenplay: they
define the plot structure, determine its progression and segment the screenplay
into thematic units (e.g., setup, complications, aftermath). We propose the
task of turning point identification in movies as a means of analyzing their
narrative structure. We argue that turning points and the segmentation they
provide can facilitate processing long, complex narratives, such as
screenplays, for summarization and question answering. We introduce a dataset
consisting of screenplays and plot synopses annotated with turning points and
present an end-to-end neural network model that identifies turning points in
plot synopses and projects them onto scenes in screenplays. Our model
outperforms strong baselines based on state-of-the-art sentence representations
and the expected position of turning points.
