A guided journey through non-interactive automatic story generation

TL;DR

This survey reviews non-interactive computational story generation methods, analyzing their approaches, contributions, and challenges, and suggests future research directions to enhance story quality and creativity.

Contribution

It provides a comprehensive overview of existing models and techniques in non-interactive story generation and identifies key areas for future research to improve story quality and creativity.

Findings

Different technological approaches have varying strengths and limitations.

Current models often lack comprehensive knowledge and creativity frameworks.

Future research should focus on autonomous idea generation and creativity criteria design.

Abstract

We present a literature survey on non-interactive computational story generation. The article starts with the presentation of requirements for creative systems, three types of models of creativity (computational, socio-cultural, and individual), and models of human creative writing. Then it reviews each class of story generation approach depending on the used technology: story-schemas, analogy, rules, planning, evolutionary algorithms, implicit knowledge learning, and explicit knowledge learning. Before the concluding section, the article analyses the contributions of the reviewed work to improve the quality of the generated stories. This analysis addresses the description of the story characters, the use of narrative knowledge including about character believability, and the possible lack of more comprehensive or more detailed knowledge or creativity models. Finally, the article presents concluding remarks in the form of suggestions of research topics that might have a significant impact on the advancement of the state of the art on autonomous non-interactive story generation systems. The article concludes that the autonomous generation and adoption of the main idea to be conveyed and the autonomous design of the creativity ensuring criteria are possibly two of most important topics for future research.