Automatic Story Generation: Challenges and Attempts (2021)

Abstract: Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. The best human-crafted stories exhibit coherent plot, strong characters, and adherence to genres, attributes that current states-of-the-art still struggle to produce, even using transformer architectures. In this paper, we analyze works in story generation that utilize machine learning approaches to (1) address story generation controllability, (2) incorporate commonsense knowledge, (3) infer reasonable character actions, and (4) generate creative language.

## introduction and motivation storytelling is central to human communication. people use stories to communicate effectively with one another. as humans, we engage with well-told stories and comprehend more information from stories @xcite . however, when it comes to automatic storytelling, computers still have a long way to go. the field of automated story generation, or computational narrative, has received more attention because of recent technological enhancements. the importance of computational narrative is that it can improve human interaction with intelligent systems. storytelling helps computers communicate with humans @xcite , and automated story generation drives improvements in natural language processing. computational narrative research involves story understanding, story representation, and story generation. in this survey, we will focus on the story generation capabilities of computational systems. many surveys were written on different facets of computational storytelling. @xcite provides a chronological summary of storytelling systems focusing on computational creativity, measured using metrics including the stories' novelty * * equal contributions and the users' involvement in the storytelling process. @xcite focuses on interactive intelligence, a digital interactive storytelling experience where users interact with the computational system to build storylines. the survey paper touches on generating narrative structures and character building. @xcite discusses humancentered computational narrative and how it can improve artificial intelligence applications. the paper shed some light on machine learning challenges concerned with story generation and commonsense reasoning. nevertheless, it does not go into these challenges in-depth as it is not its primary focus point. past survey papers focused primarily on story generation using specific approaches or on specific sub-problems in story generation. for example, @xcite summarizes progress in the areas of plot and space generation without much discussion around neural language models. @xcite examine different deep learning models used in story generation and categorize them by their goals. however, there is still motivation to organize a survey in a different manner. the process of automatically generating a logically-coherent and interesting narrative is complex. therefore, it might be more beneficial detailing the major problems present in the field and techniques used to address them rather than summarizing different types of models. for people who are new in the field, our survey should serve as a decent starting point for conducting innovative research in the field. some of the survey papers, albeit comprehensive, do not include the latest development in story generation because of transformers. @xcite chronicles interactive narrative prior to 2013, yet the discussed approaches do not include large-scale neural language models, which we have access to now and has been fueling new research in the field. another example would be the paper by @xcite , where the author comments on storytelling systems and different evaluation criteria for creativity; similarly, all of the systems consist of planning and no neural approaches. we acknowledge that more survey papers exist with different areas of focus within the domain of computational narratives, such as narrative theories @xcite , interactive intelligence @xcite , plan-based story generation @xcite . it has been demonstrated that the field of automated story generation has a gap in up-to-date survey papers. our paper, by laying out all the prominent research problems in story generation and previous literature addressing these issues, will fill this gap. the scope of this survey paper is to explore the challenges in automatic story generation. we hope to contribute in the following ways: ## conclusion and future work this survey discussed several directions in automatic story generation research and their respective challenges, and summarized research attempts at solving them. the research in automatic story generation is far from done. with automated story generation, such challenges include controlling the story content, commonsense knowledge, inferring reasonable character actions, and creativity. this survey provides a dive into some of these active research problems. in section 2.1, we summarized a few approaches addressing the problem of story generation controllability. we noticed that the papers we reviewed shared one of two goals, either controlling the story outline or controlling the story ending. we also observed an emerging trend towards using transformer-based language models for story generation. in section 2.2, we introduced methods to incorporate commonsense knowledge into story generation systems and frameworks with such integrated commonsense knowledge. frequent approaches include: (1) fine-tuning on commonsense datasets, 1 https://www.gwern.net/gpt-3 in section 2.3, we provided insight into some less-researched areas at the moment, including characters in generated narratives and the creativity of generated stories. incorporating representations of entities into the generation process seems to improve the coherence of the plot, and character affect can help navigate the generation space as well. extending the work in character affect from single character to multi characters can perhaps further enhance the generated narratives. there has not been much emphasis on the creativity of generated texts. additionally, we highlight a few future research problems that are worth exploring: