Narrative Theory for Computational Narrative Understanding (2021)

Abstract: Over the past decade, the field of natural language processing has developed a wide array of computational methods for reasoning about narrative, including summarization, commonsense inference, and event detection. While this work has brought an important empirical lens for examining narrative, it is by and large divorced from the large body of theoretical work on narrative within the humanities, social and cognitive sciences. In this position paper, we introduce the dominant theoretical frameworks to the NLP community, situate current research in NLP within distinct narratological traditions, and argue that linking computational work in NLP to theory opens up a range of new empirical questions that would both help advance our understanding of narrative and open up new practical applications.

## introduction research in nlp has seen an increasing attention to narrative understanding over the past decade. indeed, the nlp community is not alone. from studies in economics (shiller, 2020) to climate science @xcite to political polarization @xcite to mental health @xcite , there is a growing consensus that narrative is a key concept for understanding human behavior and beliefs. narrative is a core mechanism through which human beings come to understand their world, find meaning, motivate their actions and those of others, and create communities. as narratologists often highlight, narrative is a universal practice among all human cultures across all time periods @xcite . in developing computational methods to understand narrative and its various social, personal, and cultural functions, the nlp community has drawn on a wide range of theoretical perspectives on narrative (both implicit and explicit), without the benefit of situating those perspectives within a broader organizing theoretical structure. such an organizing structure can encourage terminological consistency and methodological clarity in terms of research goals, while also illuminating the points of contact between seemingly unrelated research agendas. this position paper seeks to provide the beginnings of such a unifying framework for the computational study of narrative. drawing on the multiple expertises of literary and textual studies on the one hand and natural language processing on the other, our aim is to provide an overview of the different theoretical components of narrative study and highlight promising nlp research in those domains. by providing the nlp community with a richer and more coherent theoretical foundation for implementing computational solutions for narrative understanding, our goal is to bring together otherwise disconnected lines of inquiry as well as reduce competing conceptual frameworks that can inhibit progress. in doing so, the field will be better positioned to address the major research challenges that we describe in the closing section. in what follows we organize our paper in the following way: first, we provide a general definition of narrativity, integrating existing theoretical frameworks; second, we review the fundamental components of narrative understanding and the research opportunities within each domain; third and finally, we identify a few salient higher-level problems of narrative understanding for the nlp community to address as they relate to questions of social change, creative industries, and social and mental well-being. ## scaling up narrative understanding our aim so far is to align existing work in nlp narrative understanding with theoretical frameworks in narratology. in this final section, we want to identify three core challenge areas that tackle larger questions regarding the social impact of narrative and that depend upon the successful integration of the different strands of nlp research outlined here. broadly, each of these areas attempts to understand narrative as an important basis for human behavior. ## conclusion narrative is a pervasive phenomenon that cuts across the human experience. as research in nlp has increasingly focused on developing computational models to reason about stories, we see space for existing structures in narrative theory to help provide an organizing system around these efforts. by making connections between theoretical frameworks in narratology and existing research in nlp more explicit, we hope to provide a mechanism to coordinate diverse research efforts, unify terminology and methods, and suggest major research challenges that will help drive the empirical study of narrative forward.