The field of narrative (or story) understanding and generation is one of the oldest in natural language processing (NLP) and artificial intelligence (AI), which is hardly surprising, since storytelling is such a fundamental and familiar intellectual and social activity. In recent years, the demands of interactive entertainment and interest in the creation of engaging narratives with life-like characters have provided a fresh impetus to this field. This book provides an overview of the principal problems, approaches, and challenges faced today in modeling the narrative structure of stories. The book introduces classical narratological concepts from literary theory and their mapping to computational approaches. It demonstrates how research in AI and NLP has modeled character goals, causality, and time using formalisms from planning, case-based reasoning, and temporal reasoning, and discusses fundamental limitations in such approaches. It proposes new representations for embedded narratives and fictional entities, for assessing the pace of a narrative, and offers an empirical theory of audience response. These notions are incorporated into an annotation scheme called NarrativeML. The book identifies key issues that need to be addressed, including annotation methods for long literary narratives, the representation of modality and habituality, and characterizing the goals of narrators. It also suggests a future characterized by advanced text mining of narrative structure from large-scale corpora and the development of a variety of useful authoring aids. This is the first book to provide a systematic foundation that integrates together narratology, AI, and computational linguistics. It can serve as a narratology primer for computer scientists and an elucidation of computational narratology for literary theorists. It is written in a highly accessible manner and is intended for use by a broad scientific audience that includes linguists (computational and formal semanticists), AI researchers, cognitive scientists, computer scientists, game developers, and narrative theorists.

The field of narrative (or story) understanding and generation is one of the oldest in natural language processing (NLP) and artificial intelligence (AI), which is hardly surprising, since storytelling is such a fundamental and familiar intellectual and social activity. In recent years, the demands of interactive entertainment and interest in the creation of engaging narratives with life-like characters have provided a fresh impetus to this field. This book provides an overview of the principal problems, approaches, and challenges faced today in modeling the narrative structure of stories. The book introduces classical narratological concepts from literary theory and their mapping to computational approaches. It demonstrates how research in AI and NLP has modeled character goals, causality, and time using formalisms from planning, case-based reasoning, and temporal reasoning, and discusses fundamental limitations in such approaches. It proposes new representations for embedded narratives and fictional entities, for assessing the pace of a narrative, and offers an empirical theory of audience response. These notions are incorporated into an annotation scheme called NarrativeML. The book identifies key issues that need to be addressed, including annotation methods for long literary narratives, the representation of modality and habituality, and characterizing the goals of narrators. It also suggests a future characterized by advanced text mining of narrative structure from large-scale corpora and the development of a variety of useful authoring aids. This is the first book to provide a systematic foundation that integrates together narratology, AI, and computational linguistics. It can serve as a narratology primer for computer scientists and an elucidation of computational narratology for literary theorists. It is written in a highly accessible manner and is intended for use by a broad scientific audience that includes linguists (computational and formal semanticists), AI researchers, cognitive scientists, computer scientists, game developers, and narrative theorists. Table of Contents: List of Figures / List of Tables / Narratological Background / Characters as Intentional Agents / Time / Plot / Summary and Future Directions

A review of recent computational (deep learning) approaches to understanding news and nonfiction stories.

Narrative Intelligence (NI) — the confluence of narrative, Artificial Intelligence, and media studies — studies, models, and supports the human use of narrative to understand the world. This volume brings together established work and founding documents in Narrative Intelligence to form a common reference point for NI researchers, providing perspectives from computational linguistics, agent research, psychology, ethology, art, and media theory. It describes artificial agents with narratively structured behavior, agents that take part in stories and tours, systems that automatically generate stories, dramas, and documentaries, and systems that support people telling their own stories. It looks at how people use stories, the features of narrative that play a role in how people understand the world, and how human narrative ability may have evolved. It addresses meta-issues in NI: the history of the field, the stories AI researchers tell about their research, and the effects those stories have on the things they discover. (Series B)

From literature and film to advertisements, storytelling is an important aspect of daily life. To create an impactful story, it is important to analyze the creation and generation of a storyline. Content Generation Through Narrative Communication and Simulation is a critical research publication that explores story and the application of story in various forms of media as well as the challenges of automated story. Featuring coverage on a wide range of topics such as narrative or story generation systems, the film and movie narrative generation, and narrative evaluation, this book is geared toward researchers, students, and professionals seeking current and relevant research on the influence and creation of story in media.

An argument that great expressive power of computational media arises from the construction of phantasms—blends of cultural ideas and sensory imagination. In Phantasmal Media, D. Fox Harrell considers the expressive power of computational media. He argues, forcefully and persuasively, that the great expressive potential of computational media comes from the ability to construct and reveal phantasms—blends of cultural ideas and sensory imagination. These ubiquitous and often-unseen phantasms—cognitive phenomena that include sense of self, metaphors, social categories, narrative, and poetic thinking—influence almost all our everyday experiences. Harrell offers an approach for understanding and designing computational systems that have the power to evoke these phantasms, paying special attention to the exposure of oppressive phantasms and the creation of empowering ones. He argues for the importance of cultural content, diverse worldviews, and social values in computing. The expressive power of phantasms is not purely aesthetic, he contends; phantasmal media can express and construct the types of meaning central to the human condition. Harrell discusses, among other topics, the phantasm as an orienting perspective for developers; expressive epistemologies, or data structures based on subjective human worldviews; morphic semiotics (building on the computer scientist Joseph Goguen's theory of algebraic semiotics); cultural phantasms that influence consensus and reveal other perspectives; computing systems based on cultural models; interaction and expression; and the ways that real-world information is mapped onto, and instantiated by, computational data structures. The concept of phantasmal media, Harrell argues, offers new possibilities for using the computer to understand and improve the human condition through the human capacity to imagine.

Studying narratives is an ideal method to gain a good understanding of how various aspects of human information are organized and integrated. The concept and methods of a narrative, which have been explored in narratology and literary theories, are likely to be connected with contemporary information studies in the future, including those in computational fields such as AI, and in cognitive science. This will result in the emergence of a significant conceptual and methodological foundation for various technologies of novel contents, media, human interface, etc. Post-Narratology Through Computational and Cognitive Approaches explores the new possibilities and directions of narrative-related technologies and theories and their implications on the innovative design, development, and creation of future media and contents (such as automatic narrative or story generation systems) through interdisciplinary approaches to narratology that are dependent on computational and cognitive studies. While highlighting topics including artificial intelligence, narrative analysis, and rhetoric generation, this book is ideally designed for designers, creators, developers, researchers, and advanced-level students.