Movie analysis is an umbrella term for many tasks aiming to automatically interpret, extract, and summarize the content of amovie. Potential applications include generating shorter versions of
scripts to help with the decision making process in a production company, enhancing movie recommendation engines, and notably generating movie previews.
In this talk I will introduce the task of turning point identification as a means of analyzing movie content. According to screenwriting theory, turning points (e.g., change of plans, major setback, climax)
are crucial narrative moments within a movie: they define its plot structure,determine its progression and segment it into thematic units. I will argue that turning points and the segmentation they
provide can facilitate the analysis of long, complex narratives, such as screenplays. I will further formalize the generation of a shorter version of a movie as the problem of identifying scenes with turning points and present a graph neural network model for this task based on linguistic and audiovisual information. Finally, I will discuss why the representation of screenplays as (sparse) graphs offers interpretability and exposes the morphology of different movie genres.
Mirella Lapata is professor of natural language processing in the School of Informatics at the University of Edinburgh. Her research focuses on getting computers to understand, reason with, andgenerate natural language. She is the first recipient (2009) of the BCS and Information Retrieval Specialist Group (BCS/IRSG) Karen Sparck Jones award and a Fellow of the Royal Society of Edinburgh. She has also
received best paper awards in leading NLP conferences and has served on the editorial boards of the Journal of Artificial Intelligence Research, the Transactions of the ACL, and Computational Linguistics. She was president of SIGDAT (the group that organizes EMNLP) in 2018.