  Narrative is a ubiquitous component of human communication. Understanding its
structure plays a critical role in a wide variety of applications, ranging from
simple comparative analyses to enhanced narrative retrieval, comprehension, or
reasoning capabilities. Prior research in narratology has highlighted the
importance of studying the links between cognitive and linguistic aspects of
narratives for effective comprehension. This interdependence is related to the
textual semantics and mental language in narratives, referring to characters'
motivations, feelings or emotions, and beliefs. However, this interdependence
is hardly explored for modeling narratives. In this work, we propose the task
of automatically detecting prominent elements of the narrative structure by
analyzing the role of characters' inferred mental state along with linguistic
information at the syntactic and semantic levels. We introduce a STORIES
dataset of short personal narratives containing manual annotations of key
elements of narrative structure, specifically climax and resolution. To this
end, we implement a computational model that leverages the protagonist's mental
state information obtained from a pre-trained model trained on social
commonsense knowledge and integrates their representations with contextual
semantic embed-dings using a multi-feature fusion approach. Evaluating against
prior zero-shot and supervised baselines, we find that our model is able to
achieve significant improvements in the task of identifying climax and
resolution.
