Go to ACL ARR 2025 February homepageStoryteller: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation
Abstract: Stories are central to human culture, serving to share ideas, preserve traditions, and foster connections. Automatic story generation, a key advancement in artificial intelligence (AI), offers new possibilities for creating personalized content, exploring creative ideas, and enhancing interactive experiences. However, existing methods struggle to maintain narrative coherence and logical consistency. This disconnect compromises the overall storytelling experience, underscoring the need for substantial improvements. Inspired by human cognitive processes, we introduce Storyteller, a novel approach that systemically improves the coherence and consistency of automatically generated stories. Storyteller introduces a plot node structure based on linguistically grounded subject-verb-object (SVO) triplets, which capture essential story events and ensure a consistent logical flow. Unlike previous methods, Storyteller integrates two dynamic modules—the Storyline and narrative entity knowledge graph (NEKG)—that continuously interact with the story generation process. This integration produces structurally sound, cohesive and immersive narratives. Extensive experiments demonstrate that Storyteller significantly outperforms existing approaches, achieving an 84.33\% average win rate through human preference evalutaion. At the same time, it is also far ahead in other aspects including creativity, coherence, engagement, and realvance. Our code and data will be made publicly available upon publication.
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: Story Generation, Large Language Models
Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models, Data analysis
Languages Studied: English
Submission Number: 4737
Loading