---
title: "Text-to-Text Automatic Story Generation: A Survey (2026)"
description: "This survey maps recent work on generating stories from text input with little or no human effort. 81 references."
authors: ["Yuan Ma", "Richard Susilo", "Patrik Haslum", "Hanna Suominen"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)"
source: "https://aclanthology.org/2026.eacl-srw.39/"
doi: "10.18653/v1/2026.eacl-srw.39"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ma-2026-text-text-automatic-story-generation/"
---
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# Text-to-Text Automatic Story Generation: A Survey

**Authors:** Yuan Ma, [Richard Susilo](https://md.research.elamur.ai/free/en/authors/richard-susilo/), [Patrik Haslum](https://md.research.elamur.ai/free/en/authors/patrik-haslum/) and [Hanna Suominen](https://md.research.elamur.ai/free/en/authors/hanna-suominen/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-srw.39/) · [PDF](https://aclanthology.org/2026.eacl-srw.39.pdf) · [DOI 10.18653/v1/2026.eacl-srw.39](https://doi.org/10.18653/v1/2026.eacl-srw.39) · [OpenAlex](https://openalex.org/W7140121581)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
This survey maps recent work on generating stories from text input with little or no human effort. The task still struggles with long-range coherence, consistent characters, varied storylines and plot control. The authors ran a systematic search covering January 2021 to April 2025. They queried the ACL Anthology by title and abstract and five major venues by title only. They kept only English text-to-text studies, excluding image-to-story work. Of the 57 papers retained, some test how well LLMs can tell stories, and the rest are sorted into single-agent, multi-agent and human-in-the-loop generation methods. The review also counts which datasets and evaluation metrics appear most often.

The main finding is that LLMs now dominate the field. Every included study uses Transformer-based models, and older planning-based pipelines have effectively disappeared. Datasets and evaluation have not kept pace. The authors call for better metrics, more suitable corpora, and continued work on long-form coherence in real applications. The survey gives a compact overview of the area for anyone starting story-generation research.

## Key points
- The corpus is 57 peer-reviewed papers drawn from the ACL Anthology plus ACL, EMNLP, NAACL-HLT, TACL and COLING. Two reviewers screened titles and abstracts, and the process followed a PRISMA flow.
- Methods are grouped as single-agent, multi-agent or human-in-the-loop. Single-agent work is further split by focus on coherence, creativity or controllability, often using hierarchical outline-then-expand designs such as Yang et al.'s GPT-3 framework.
- All reviewed studies rely on Transformer language models, and over half use LLMs specifically. None depends solely on structural or planning-based approaches.
- The review quantifies five frequently used datasets and nine metrics. ROCStories and WritingPrompts remain the most common datasets, and no newer dataset has gained wide uptake. Their stories reach only a few thousand words, are written by non-professionals, and risk LLM data contamination.
- Human evaluation is still the most trusted method. LLM-as-judge evaluation is spreading but has no standardized rubric. Human studies of the stories themselves found GPT-4 rated below a professional novelist and LLMs weaker on originality and turning points.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Australian National University](https://md.research.elamur.ai/free/en/universities/australian-national-university/) — University, Canberra, Australia · 23 works · 6,522 citations

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## Cite

Cite as: Yuan Ma, Richard Susilo, Patrik Haslum and Hanna Suominen. 2026. Text-to-Text Automatic Story Generation: A Survey. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ma2026text,
  title = {{Text-to-Text Automatic Story Generation: A Survey}},
  author = {Yuan Ma, Richard Susilo, Patrik Haslum and Hanna Suominen},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-srw.39},
  url = {https://doi.org/10.18653/v1/2026.eacl-srw.39}
}
```

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*Updated 1 November 2026*