This study presents a theory-inspired visual narrative generative system that integrates conceptual principles-comic authoring idioms-with generative and language models to enhance the comic creation process. Our system combines human creativity with AI models to support parts of the generative process, providing a collaborative platform for creating comic content. These comic-authoring idioms, derived from prior human-created image sequences, serve as guidelines for crafting and refining storytelling. The system translates these principles into system layers that facilitate comic creation through sequential decision-making, addressing narrative elements such as panel composition, story tension changes, and panel transitions. Key contributions include integrating machine learning models into the human-AI cooperative comic generation process, deploying abstract narrative theories into AI-driven comic creation, and a customizable tool for narrative-driven image sequences. This approach improves narrative elements in generated image sequences and engages human creativity in an AI-generative process of comics. We open-source the code at https://github.com/RimiChen/Collaborative_Comic_Generation.

Computational narrative systems usually require knowledge about the story world and narrative theory to be encoded in some form of structured knowledge representation formalism, a notoriously time-consuming task requiring expertise in both storytelling and knowledge engineering. In this paper we present an approach that combines supervised machine learning with narrative domain knowledge toward automatically extracting such knowledge from natural language stories, focusing specifically on predicting Proppian narrative functions. Our experiments on a dataset of Russian fairy tales show that our system outperforms an informed baseline and that combining top-down narrative theory and bottom-up statistical models inferred from an annotated dataset increases prediction accuracy with respect to using them in isolation.

Narratives are fundamental to our perception of the world and are pervasive in all activities that involve the representation of events in time. Yet, modern online information systems do not incorporate narratives in their representation of events occurring over time. This article aims to bridge this gap, combining the theory of narrative representations with the data from modern online systems. We make three key contributions: a theory-driven computational representation of narratives, a novel extraction algorithm to obtain these representations from data, and an evaluation of our approach. In particular, given the effectiveness of visual metaphors, we employ a route map metaphor to design a narrative map representation. The narrative map representation illustrates the events and stories in the narrative as a series of landmarks and routes on the map. Each element of our representation is backed by a corresponding element from formal narrative theory, thus providing a solid theoretical background to our method. Our approach extracts the underlying graph structure of the narrative map using a novel optimization technique focused on maximizing coherence while respecting structural and coverage constraints. We showcase the effectiveness of our approach by performing a user evaluation to assess the quality of the representation, metaphor, and visualization. Evaluation results indicate that the Narrative Map representation is a powerful method to communicate complex narratives to individuals. Our findings have implications for intelligence analysts, computational journalists, and misinformation researchers.

BERLIN stands for Behavioural Event Reconstruction Linguistic Interface for Narratives. I introduced BERLIN a few blogs ago - in my "final blog." Theoretically after one's final blog, no further blogs are forthcoming. However, I am now posting bonus blogs reflecting aspects of the same closing subject. Today, I will be elaborating on BERLIN's syntax and how its searches are facilitated. As a general rule, the objective of BERLIN is to convert human-friendly narrative into computer-friendly code. It isn't unusual for computer code to be expressed in a human-like language for the purpose of executing a program. A computer program has rigid parameters. BERLIN on the other hand is designed to support "expression." Rather than the code adapting to the needs of a computer program, it is adapted to the requirements of expression. BERLIN remains shaped by a type of program of sorts.