@inproceedings{li-etal-2025-storyteller,
title = "{STORYTELLER}: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation",
author = "Li, Jiaming and
Chen, Yukun and
Liu, Ziqiang and
Tan, Minghuan and
Zhang, Lei and
Li, Yunshui and
Luo, Run and
Chen, Longze and
Luo, Jing and
Argha, Ahmadreza and
Alinejad-Rokny, Hamid and
Zhou, Wei and
Yang, Min",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1071/",
doi = "10.18653/v1/2025.findings-acl.1071",
pages = "20818--20846",
ISBN = "979-8-89176-256-5",
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 evaluation. At the same time, it is also far ahead in other aspects including creativity, coherence, engagement, and relevance."
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<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 evaluation. At the same time, it is also far ahead in other aspects including creativity, coherence, engagement, and relevance.</abstract>
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%0 Conference Proceedings
%T STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation
%A Li, Jiaming
%A Chen, Yukun
%A Liu, Ziqiang
%A Tan, Minghuan
%A Zhang, Lei
%A Li, Yunshui
%A Luo, Run
%A Chen, Longze
%A Luo, Jing
%A Argha, Ahmadreza
%A Alinejad-Rokny, Hamid
%A Zhou, Wei
%A Yang, Min
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F li-etal-2025-storyteller
%X 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 evaluation. At the same time, it is also far ahead in other aspects including creativity, coherence, engagement, and relevance.
%R 10.18653/v1/2025.findings-acl.1071
%U https://aclanthology.org/2025.findings-acl.1071/
%U https://doi.org/10.18653/v1/2025.findings-acl.1071
%P 20818-20846
Markdown (Informal)
[STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation](https://aclanthology.org/2025.findings-acl.1071/) (Li et al., Findings 2025)
ACL
- Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang, Yunshui Li, Run Luo, Longze Chen, Jing Luo, Ahmadreza Argha, Hamid Alinejad-Rokny, Wei Zhou, and Min Yang. 2025. STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 20818–20846, Vienna, Austria. Association for Computational Linguistics.