Automatic movie analysis is the task of employing Machine Learning methods to the
field of screenplays, movie scripts, and motion pictures to facilitate or enable various
tasks throughout the entirety of a movie’s life-cycle. From helping with making
informed decisions about a new movie script with respect to aspects such as its originality,
similarity to other movies, or even commercial viability, all the way to offering
consumers new and interesting ways of viewing the final movie, many stages in the
life-cycle of a movie stand to benefit from Machine Learning techniques that promise
to reduce human effort, time, or both. Within this field of automatic movie analysis,
this thesis addresses the task of summarising the content of screenplays, enabling users
at any stage to gain a broad understanding of a movie from greatly reduced data. The
contributions of this thesis are four-fold: (i)We introduce ScriptBase, a new large-scale
data set of original movie scripts, annotated with additional meta-information such as
genre and plot tags, cast information, and log- and tag-lines. To our knowledge, Script-
Base is the largest data set of its kind, containing scripts and information for almost
1,000 Hollywood movies. (ii) We present a dynamic summarisation model for the
screenplay domain, which allows for extraction of highly informative and important
scenes from movie scripts. The extracted summaries allow for the content of the original
script to stay largely intact and provide the user with its important parts, while
greatly reducing the script-reading time. (iii) We extend our summarisation model
to capture additional modalities beyond the screenplay text. The model is rendered
multi-modal by introducing visual information obtained from the actual movie and by
extracting scenes from the movie, allowing users to generate visual summaries of motion
pictures. (iv) We devise a novel end-to-end neural network model for generating
natural language screenplay overviews. This model enables the user to generate short
descriptive and informative texts that capture certain aspects of a movie script, such as
its genres, approximate content, or style, allowing them to gain a fast, high-level understanding
of the screenplay. Multiple automatic and human evaluations were carried
out to assess the performance of our models, demonstrating that they are well-suited
for the tasks set out in this thesis, outperforming strong baselines. Furthermore, the
ScriptBase data set has started to gain traction, and is currently used by a number of
other researchers in the field to tackle various tasks relating to screenplays and their
analysis