A4 Refereed article in a conference publication
Text-to-Text Automatic Story Generation: A Survey; 
Authors: Ma, Yuan; Susilo, Richard; Haslum, Patrik; Suominen, Hanna
Editors: Santamaria, Selene Baez; Somayajula, Sai Ashish; Yamaguchi, Atsuki
Conference name: European Chapter of the Association for Computational Linguistics
Publisher: Association for Computational Linguistics
Publication year: 2026
Book title : Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics : (Volume 4: Student Research Workshop)
First page : 514
Last page: 527
ISBN: 979-8-89176-383-8
DOI: https://doi.org/10.18653/v1/2026.eacl-srw.39
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Open Access publication channel
Web address : https://doi.org/10.18653/v1/2026.eacl-srw.39
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/527033444
Self-archived copy's licence: CC BY
Self-archived copy's version: Publisher`s PDF
Automatic story generation aims to produce coherent, engaging, and contextually consistent narratives with minimal or no human involvement, thereby advancing research in computational creativity and applications in human language technologies. The emergence of large language models has progressed the task, enabling systems to generate multi-thousandword stories under diverse constraints. Despite these advances, maintaining narrative coherence, character consistency, storyline diversity, and plot controllability in generating stories is still challenging. In this survey, we conduct a systematic review of research published over the past four years to examine the major trends and key limitations in story generation methods, model architectures, datasets, and evaluation methodologies. Based on this analysis of 57 included papers, we propose developing new evaluation metrics and creating more suitable datasets, together with ongoing improvement of narrative coherence and consistency, as well as their exploration in practical applications of story generation, as actions to support continued progress in automatic story generation.
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Funding information in the publication:
We thank The Australian National University (ANU) and the ANU School of Computing for supporting the PhD studies of the first two authors.
