  Stories interest us not because they are a sequence of mundane and
predictable events but because they have drama and tension. Crucial to creating
dramatic and exciting stories are surprise and suspense. The thesis trains a
series of deep learning models via only reading stories, a self-supervised (or
unsupervised) system. Narrative theory methods (rules and procedures) are
applied to the knowledge built into deep learning models to directly infer
salience, surprise, and salience in stories. Extensions add memory and external
knowledge from story plots and from Wikipedia to infer salience on novels such
as Great Expectations and plays such as Macbeth. Other work adapts the models
as a planning system for generating original stories.
  The thesis finds that applying the narrative theory to deep learning models
can align with the typical reader. In follow-up work, the insights could help
improve computer models for tasks such as automatic story writing and
assistance for writing, summarising or editing stories. Moreover, the approach
of applying narrative theory to the inherent qualities built in a system that
learns itself (self-supervised) from reading from books, watching videos, and
listening to audio is much cheaper and more adaptable to other domains and
tasks. Progress is swift in improving self-supervised systems. As such, the
thesis's relevance is that applying domain expertise with these systems may be
a more productive approach for applying machine learning in many areas of
interest.
