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.

In this important contribution to narrative theory, Marie-Laure Ryan applies insights from artificial intelligence and the theory of possible worlds to the study of narrative and fiction. For Ryan, the theory of possible worlds provides a more nuanced way of discussing the commonplace notion of a fictional "world," while artificial intelligence contributes to narratology and the theory of fiction directly via its researches into the congnitive processes of texts and automatic story generation. Although Ryan applies exotic theories to the study of narrative and to fiction, her book maintains a solid basis in literary theory and makes the formal models developed by AI researchers accessible to the student of literature. By combining the philosophical background of possible world theory with models inspired by AI, the book fulfills a pressing need in narratology for new paradigms and an interdisciplinary perspective.

This volume describes the theoretical and empirical results of a seven year collaborative effort of cognitive scientists to develop a computational model for narrative understanding. Disciplines represented include artificial intelligence, cognitive psychology, communicative disorders, education, English, geography, linguistics, and philosophy. The book argues for an organized representational system -- a Deictic Center (DC) -- which is constructed by readers from language in a text combined with their world knowledge. As readers approach a new text they need to gather and maintain information about who the participants are and where and when the events take place. This information plays a central role in understanding the narrative. The editors claim that readers maintain this information without explicit textual reminders by including it in their mental model of the story world. Because of the centrality of the temporal, spatial, and character information in narratives, they developed their notion of a DC as a crucial part of the reader's mental model of the narrative. The events that carry the temporal and spatial core of the narrative are linguistically and conceptually constrained according to certain principles that can be relatively well defined. A narrative obviously unfolds one word, or one sentence, at a time. This volume suggests that cognitively a narrative usually unfolds one place and time at a time. This spatio-temporal location functions as part of the DC of the narrative. It is the "here" and "now" of the reader's "mind's eye" in the world of the story. Organized into seven parts, this book describes the goal of the cognitive science project resulting in this volume, the methodological approaches taken, and the history of the collaborative effort. It provides a historical and theoretical background underlying the DC theory, including discussions of deixis in language and the nature of fiction. It goes on to outline the computational framework and how it is used to represent deixis in narrative, and details the linguistic devices implicated in the DC theory. Other subjects covered include: crosslinguistic indicators of subjectivity, psychological investigations of the use of deixis by children and adults as they process narratives, conversation, direction giving, implications for emerging literacy, and a narrator's experience in writing a short story.

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.

Storylistening makes the case for the urgent need to take stories seriously in order to improve public reasoning. Dillon and Craig provide a theory and practice for gathering narrative evidence that will complement and strengthen, not distort, other forms of evidence, including that from science. Focusing on the cognitive and the collective, Dillon and Craig show how stories offer alternative points of view, create and cohere collective identities, function as narrative models, and play a crucial role in anticipation. They explore these four functions in areas of public reasoning where decisions are strongly influenced by contentious knowledge and powerful imaginings: climate change, artificial intelligence, the economy, and nuclear weapons and power. Vivid performative readings of stories from The Ballad of Tam-Lin to The Terminator demonstrate the insights that storylistening can bring and the ways it might be practised. The book provokes a reimagining of what a public humanities might look like, and shows how the structures and practices of public reasoning can evolve to better incorporate narrative evidence. Storylistening aims to create the conditions in which the important task of listening to stories is possible, expected, and becomes endemic. Taking the reader through complex ideas from different disciplines in ways that do not require any prior knowledge, this book is an essential read for policymakers, political scientists, students of literary studies, and anyone interested in the public humanities and the value, importance, and operation of narratives.

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