---
title: "DOC — paper by Yang et al. (2023) — summary and ideas"
description: "The authors present DOC (Detailed Outline Control), which builds on the planning-and-drafting… 42 references, cited by 53 papers here."
authors: ["Kevin Yang", "Dan Klein", "Nanyun Peng", "Yuandong Tian"]
year: 2023
venue: "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2023.acl-long.190/"
doi: "10.18653/v1/2023.acl-long.190"
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/yang-2023-doc-improving-long-story-coherence/"
---
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# DOC: Improving Long Story Coherence With Detailed Outline Control

**Authors:** [Kevin Yang](https://md.research.elamur.ai/free/en/authors/kevin-yang/), [Dan Klein](https://md.research.elamur.ai/free/en/authors/dan-klein/), [Nanyun Peng](https://md.research.elamur.ai/free/en/authors/nanyun-peng/) and [Yuandong Tian](https://md.research.elamur.ai/free/en/authors/yuandong-tian/) · **Year:** 2023 · **Venue:** [Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2023](https://md.research.elamur.ai/free/en/venues/acl/2023/)) · **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/2023.acl-long.190/) · [PDF](https://aclanthology.org/2023.acl-long.190.pdf) · [DOI 10.18653/v1/2023.acl-long.190](https://doi.org/10.18653/v1/2023.acl-long.190) · [OpenAlex](https://openalex.org/W4385572460)

**Citations:** 29 citations (OpenAlex, 2026-10-14) · [53](https://md.research.elamur.ai/free/en/papers/yang-2023-doc-improving-long-story-coherence/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Automatically generated stories that run to several thousand words often read smoothly at the passage level but lose the overall plot, drifting away from the intended plan. The authors present DOC (Detailed Outline Control), which builds on the planning-and-drafting pipeline of Re 3 and adds two complementary modules. The first is a detailed outliner, which uses structured prompting to recursively expand a short three-point outline into a hierarchical tree. Each node in the tree is filtered and reranked for coherence and tagged with a setting and characters. This moves much of the creative work into the planning stage. The second is a detailed controller, a FUDGE-style OPT-350m model trained contrastively. During drafting, it steers each token so that passages remain aligned with their outline items. In pairwise human evaluations of stories averaging over 3500 words, DOC clearly beat Re 3 on plot coherence, outline relevance and interestingness. Annotators also found it easier to control when they edited plans interactively. The work shows that richer planning paired with strong control is a practical step toward coherent book-length generation.

## Key points
- DOC outperformed Re 3 in human evaluations by absolute margins of 22.5% for plot coherence, 28.2% for outline relevance and 20.7% for interestingness. Ablations show that both the outliner and the controller are necessary.
- The outliner expands the outline tree breadth-first, usually producing two or three children per node, down to a preset depth. Because the depth is adjustable, the amount of detail can scale with the desired story length.
- The controller adapts FUDGE using an OPT-350m model trained to match summaries with passage prefixes. Fluent hard negatives in training keep long passages on topic from beginning to end. Drafting relies on large LMs such as GPT3-175B, and Re 3's editing step is dropped.
- In a human-in-the-loop setting, users interact with DOC at the outline level rather than passage by passage, and they strongly preferred it over Re 3.
- Code, models and all collected human annotations are released at github.com/yangkevin2/doc-story-generation. Known limitations are that the system is English-only, depends on costly human evaluation, and still makes occasional factual inconsistencies such as wrong names or genders.

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

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## Cite

Cite as: Kevin Yang, Dan Klein, Nanyun Peng and Yuandong Tian. 2023. DOC: Improving Long Story Coherence With Detailed Outline Control. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{yang2023doc,
  title = {{DOC: Improving Long Story Coherence With Detailed Outline Control}},
  author = {Kevin Yang, Dan Klein, Nanyun Peng and Yuandong Tian},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2023},
  doi = {10.18653/v1/2023.acl-long.190},
  url = {https://doi.org/10.18653/v1/2023.acl-long.190}
}
```

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*Updated 24 October 2026*