Hierarchical Neural Story Generation

Angela FanMike LewisYann Dauphin

article2018ACL2,105 citations

Presents a hierarchical story generation framework and a 300,000-example prompt-story dataset that substantially improves narrative coherence by first generating a high-level premise before expanding it into full text using gated multi-scale self-attention.

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Automated story generation represents a major frontier in artificial intelligence, requiring systems to maintain thematic consistency across long passages and plan high-level narrative plots. Traditional sequential text generation models struggle with these demands, often drifting off-topic or degenerating into generic phrasing because they generate text strictly word-by-word without broader context. Addressing this challenge is vital for advancing creative AI tools, automated content creation, and long-form narrative modeling.

The article demonstrates and evaluates a hierarchical neural architecture designed to produce fluent, topically relevant stories by decomposing generation into high-level premise creation followed by full-passage drafting. To support this, the authors introduce a new large-scale dataset, a gated multi-scale self-attention mechanism to capture long-range context efficiently, and a model fusion technique that enforces strict relevance between the generated story and its guiding premise.

The authors constructed a dataset of 303,358 human-written stories paired with writing prompts scraped from Reddit's WritingPrompts community, representing over 200 million words. Using this resource, they implemented a two-step framework: first generating a prompt using a convolutional language model, and then generating the corresponding story via a convolutional sequence-to-sequence model. The system incorporates deep multi-scale self-attention to capture past context across various time scales and leverages model fusion—training a second sequence model over a fixed, pretrained model—to ensure the generated narrative adheres to the prompt rather than defaulting to generic text. Evaluation relied on perplexity metrics, prompt-ranking accuracy across 1,000 test cases, and blind human evaluation studies on Amazon Mechanical Turk comparing story preference and prompt alignment.

The evaluation yielded several key findings. First, human evaluators preferred stories produced by the hierarchical model over a non-hierarchical baseline by more than two to one (67.3% preference). Second, combining the gated multi-scale self-attention mechanism with model fusion reduced test perplexity significantly, dropping it by roughly 9 points compared to standard convolutional baselines (from 45.54 down to 36.56). Third, human judges matched stories to their source prompts with substantially higher accuracy when using the fusion model, improving pairing performance by 7% over standard ensembling. Finally, while retrieval baselines quickly lose topical relevance as more stories are created, the generative fusion model sustains consistent relevance across an unlimited number of generated samples while maintaining lower word-overlap copying rates.

These findings indicate that introducing an explicit planning hierarchy alongside residual fusion architectures substantially mitigates the tendency of sequence models to lose coherence over multi-paragraph texts. By enabling the primary network to focus on narrative premises while the fused secondary layer generates rare, prompt-specific words, this strategy reduces the risk of repetitive, generic outputs. Consequently, the approach significantly improves text generation performance without requiring costly retrieval search over massive static databases.

Organizations developing creative AI tools or long-form document generators should adopt hierarchical planning architectures and multi-scale attention rather than single-pass language models. When implementing these systems, teams should utilize restricted top-k random sampling rather than beam search to avoid repetitive phrasing. Further work should focus on refining premise-generation models to produce more diverse, imaginative concepts, as well as addressing formatting artifacts and local repetition.

Confidence in these findings is high given the large dataset and strong alignment between automated metrics and human evaluations. However, practitioners should note certain limitations: prompt generation remains prone to producing common, generic themes, and sampling occasionally introduces minor grammatical artifacts, dialogue formatting errors, or local word repetition.

Meta attention mechanism hierarchical model hierarchical structures hierarchical neural story generation hierarchical text generation multi-scale attention

arXiv: 1805.04833 facebookresearch/fairseq

No sufficiently relevant recommendations were found.

Paper: Re3: Generating Longer Stories With Recursive Reprompting and Revision, Kevin Yang et al. (2022). Re3 carries the source’s premise-to-story planning idea into longer narratives, adding recursive drafting, revision, and consistency checks to sustain plots across thousands of words.

Cover for Hierarchical Neural Story Generation

Abstract

We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text. We gain further improvements with a novel form of model fusion that improves the relevance of the story to the prompt, and adding a new gated multi-scale self-attention mechanism to model long-range context. Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one.

Topics

attention mechanism hierarchical model hierarchical structures human evaluation

Keywords

hierarchical neural story generation hierarchical text generation multi-scale attention language modeling event representations prompt generation long-range context

1 Introduction
2 Writing Prompts Dataset
3 Approach
3.1 Hierarchical Story Generation
3.2 Efficient Learning with Convolutional Sequence-to-Sequence Model
3.3 Modeling Unbounded Context with Gated Multi-Scale Self-attention
3.4 Improving Relevance to Input Prompt with Model Fusion
4 Related Work
4.1 Story Generation
4.2 Hierarchical Text Generation
4.3 Fusion Models
5 Experimental Setup
5.1 Baselines
5.2 Fusion Training
5.3 Training
5.4 Generation
5.5 Evaluation
6 Results
7 Discussion
7.1 Generation Quality
7.2 Use of Attention
8 Conclusion
References
9 Appendix of Model Architectures
9.1 GCNN Language Model + Self-Attention
9.2 Conv seq2seq + self-attention
9.3 Ensemble: Conv seq2seq + self-attention
9.4 Fusion: Conv seq2seq + self-attention

Knowls

Knowl 1 — Hierarchical Story Generation Framework
model/method
Hierarchical story generation decomposes open-ended creative story writing into a two-level generation pipeline:
1. Prompt/Premise Generation: A convolutional language model (such as a Gated Convolutional Neural Network) first generates a high-level sentence or premise $\mathbf{p} = (p_1, \dots, p_M)$ that establishes the plot structure, topic, and character setting of the story.
2. Story Generation: A sequence-to-sequence (seq2seq) model conditions on the generated premise $\mathbf{p}$ to produce the full passage of story text $\mathbf{s} = (s_1, \dots, s_N)$ .
Conditioning on an explicit textual premise provides high-level plot grounding, prevents language models from drifting off-topic across long texts, and enables macro-level narrative planning beyond local next-word prediction.
Knowl 2 — Model Fusion for Sequence-to-Sequence Story Generation
model/method
To prevent sequence-to-sequence models from ignoring the input prompt and degenerating into unconditional language models, model fusion integrates a fixed, pre-trained sequence-to-sequence model with a secondary sequence-to-sequence model trained concurrently.

At decoder timestep $t$ , let $h_t^{\text{Pretrained}}$ denote the hidden state of the pre-trained seq2seq model and $h_t^{\text{Training}}$ denote the hidden state of the model being trained. Dynamic gating vectors $g_t$ are computed via a linear projection of the concatenated hidden states followed by a sigmoid activation:

$g_t = \sigma\left(W [h_t^{\text{Training}}; h_t^{\text{Pretrained}}] + b\right)$

$h_t = g_t \odot [h_t^{\text{Training}}; h_t^{\text{Pretrained}}]$

where $W$ and $b$ are trainable weights and biases, and $\odot$ is element-wise multiplication. The gated representation $h_t$ is passed through fully connected layers with Gated Linear Unit (GLU) activations and layer normalization before final vocabulary projection.

This mechanism acts as a residual/boosting learner: the pre-trained seq2seq network handles standard grammar and frequent words, forcing the secondary network to focus on prompt dependencies and domain-specific vocabulary.
Knowl 3 — Gated Multi-Scale Self-Attention for Convolutional Decoders
model/method
To overcome the bounded context window of standard convolutional decoders without recurrent bottlenecking, decoders are augmented with multi-head gated self-attention operating over past generation timesteps at varying time scales.

Key components of the self-attention mechanism include:
- Gated Transformations: Queries $q$ , keys $k$ , and values $v$ are computed using deep neural sub-networks with Gated Linear Unit (GLU) activations rather than standard linear projections, providing fine-grained selection capacity.
- Multi-Scale Downsampling: Different attention heads operate at distinct temporal scales by downsampling inputs by varying stride factors (e.g., head 1 receives every timestep, head 2 every second timestep, head 3 every third timestep), enforcing distinct temporal resolution per head and sharpening attention distributions.
- Null Attention Option and Past Masking: The model can optionally attend to a $\mathbf{0}$ vector if past context is uninformative, allowing the architecture to revert to local convolution. Attention is masked strictly to past timesteps $0, \dots, t-1$ .
The output representation of a single attention head for hidden states $h_{0:t}^L$ at decoder layer $L$ is:

$h_{0:t}^{L+1} = \text{Linear}\left( v(h_{0:t-1}^L) \odot \text{softmax}\left( q(h_{0:t}^L) k(h_{0:t}^L)^\top \right) \right)$

Outputs across multiple heads are concatenated and linearly projected.

Knowl 4 — WritingPrompts Story Generation Benchmark Dataset

data/table

The WRITINGPROMPTS dataset is a large corpus scraped from Reddit's r/WritingPrompts community over three years, pairing diverse user-submitted writing prompts with creative story responses.

Stories were filtered to remove moderator notices, automated bot posts, deleted submissions, and stories shorter than 30 words. Prompts and stories are tokenized with NLTK, limiting the vocabulary to tokens appearing more than 10 times (yielding 19,025 prompt tokens and 104,960 story tokens). Stories are truncated to a maximum length of 1,000 words.

Knowl 5 — Top-k Random Sampling Generation Strategy
algorithm
To prevent repetition, generic phrase loops, and truncation associated with beam search, stories are generated using top- $k$ random sampling combined with softmax temperature scaling.
Input: Decoder model parameterized with vocabulary $V$ , context prompt $\mathbf{p}$ , top candidates cutoff $k = 10$ , temperature $\tau > 0$ , max generation length $T$ , end-of-story token $\text{<eos>}$
Output: Generated story tokens $s_{1:L}$
$s_0 \leftarrow \text{<sos>}$
$t \leftarrow 1$
while $t \le T$ do
     $z_t \leftarrow \text{DecoderLogits}(s_{0:t-1}, \mathbf{p})$
     $p_t(w) \leftarrow \frac{\exp(z_{t,w} / \tau)}{\sum_{v \in V} \exp(z_{t,v} / \tau)} \quad \forall w \in V$
     $V_k \leftarrow \{w \in V : p_t(w) \text{ is among the top } k \text{ largest probabilities}\}$
     $P_k(w) \leftarrow \frac{p_t(w)}{\sum_{u \in V_k} p_t(u)} \quad \forall w \in V_k$
     $s_t \sim \text{Categorical}(V_k, P_k)$
    if $s_t = \text{<eos>}$ then
        break
    end if
     $t \leftarrow t + 1$
end while
return $s_{1:t}$

Knowl 6 — Language Modeling Perplexity on WritingPrompts

data/table

Language modeling perplexity results evaluated on the WRITINGPROMPTS dataset show that sequence-to-sequence convolutional architectures with self-attention and model fusion substantially outperform recurrent baselines and standard convolutional seq2seq models.

Model	# Parameters (mil)	Valid Perplexity	Test Perplexity
GCNN LM	123.4	54.50	54.79
GCNN + self-attention LM	126.4	51.84	51.18
LSTM seq2seq	110.3	46.83	46.79
Conv seq2seq	113.0	45.27	45.54
Conv seq2seq + self-attention	134.7	37.37	37.94
Ensemble: Conv seq2seq + self-attention	270.3	36.63	36.93
Fusion: Conv seq2seq + self-attention	255.4	36.08	36.56

The fusion model achieves the lowest validation and test perplexity (36.08 and 36.56), outperforming both an ensemble of two self-attentive convolutional seq2seq models (36.93 test perplexity) with fewer total parameters, and improving upon the basic Conv seq2seq model by approximately 9 perplexity points.

Knowl 7 — Component Ablation of Gated Multi-Scale Self-Attention

data/table

Ablation of individual architectural additions to the self-attention mechanism within the convolutional seq2seq decoder reveals cumulative improvements in validation and test perplexity on WRITINGPROMPTS:

Adding baseline self-attention reduces test perplexity by 3.22 points. Implementing multi-head attention provides an additional 1.93 point drop, multi-scale downsampling adds another 1.48 point drop, and GLU gating across queries, keys, and values provides an additional 0.97 point drop, totaling a 7.6 point reduction over standard convolutional seq2seq.

Knowl 8 — Human and Automatic Evaluation of Story Adherence and Hierarchical Generation
empirical result
Empirical evaluations on WRITINGPROMPTS demonstrate strong gains in topical adherence and human narrative preference when using model fusion and hierarchical generation:
- Human Triple-Pairing Task: Human evaluators matched 105 generated 150-word stories back to their original test prompts (presented in shuffled groups of 3 with 15 judges per question). Human accuracy was 72.3% for the Fusion model (approaching human ground truth stories at 88.4%), compared to 66.1% for an Ensemble, 64.8% for Conv Seq2Seq + Self-Attention, 51.4% for Conv Seq2Seq, 71.9% for 1-NN retrieval, and 17.0% for unconditional GCNN + Self-Attention LM.
- Prompt Ranking Accuracy: When scoring the likelihood of 1,000 test stories decoded under 10 candidate prompts (1 true, 9 random distractors), the Fusion model identified the true prompt with 16.3% accuracy, outperforming the Ensemble (13.8%), Conv Seq2Seq + Self-Att (12.6%), and baseline Conv Seq2Seq (10.7%).
- Hierarchical Generation Blind Preference: In a blind A/B evaluation of 400 story pairs evaluated by 5 judges each, human raters preferred stories generated by the hierarchical system (GCNN LM prompt generation followed by fusion seq2seq story generation) over an unconditional GCNN LM by a margin of 67.32% to 32.68% (over 2 to 1).
Knowl 9 — Text Novelty and Story Diversity vs. Retrieval Baselines
empirical result
Evaluation of generative story models versus retrieval baselines (TF-IDF FASTTEXT vectors queried with FAISS $k$ -nearest neighbors) indicates:
- Novelty vs. Copying: On 500 generated 150-word test stories, the average Longest Common Subsequence (LCS) between the generated text and the training set was 8.9 words for the Fusion model, 10.2 words for the standard Conv seq2seq model, and 150 words for the $k$ -NN baseline (which copies entire training stories verbatim).
- Prompt Diversity: When evaluating the connection between a prompt and multiple generated stories ( $k$ -th best story ranking), $k$ -NN relevance degrades sharply after the single nearest neighbor, whereas the generative fusion model sustains prompt topicality across repeated story generations.
Knowl 10 — Limitations of Hierarchical Story Generation and Sampling
limitation
The hierarchical neural story generation framework exhibits several specific operational limitations:
- Subword and Formatting Artifacts: Top- $k$ sampling occasionally samples isolated subword tokens without subsequent completions (e.g., generating ca without n't) or fails to output delimiter/newline tokens between consecutive dialogue turns.
- Repetition Loops: The self-attention mechanism frequently attends to recently generated words rather than distant context, leading to repetitive phrases and local looping.
- Generic Prompt Generation: The prompt-generating language model struggles with rare vocabulary and named entities characteristic of creative fantasy domains (e.g., Harry Potter or Game of Thrones), falling back onto generic sentence openings (e.g., frequently opening with The man...).
- Static Encoder Attention: Unlike machine translation where encoder attention shifts continuously across decoding steps, encoder-decoder attention maps in story fusion remain largely stationary throughout the entire generation process, focusing continuously on a small set of salient prompt keywords.

Coverage note — None: all main contributions (the WRITINGPROMPTS dataset, the hierarchical generation framework, gated multi-scale self-attention, model fusion, top-k decoding, quantitative perplexity and ablation tables, human and automatic adherence benchmarks, and qualitative error analyses) are fully represented.

References

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Citation

MLA

Fan, A., et al. “Hierarchical Neural Story Generation”. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018, pp. 889–98, https://doi.org/10.18653/v1/P18-1082.

APA

Fan, A., Lewis, M., & Dauphin, Y. (2018). Hierarchical Neural Story Generation. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 889–898. https://doi.org/10.18653/v1/P18-1082

Chicago

Fan, A., M. Lewis, and Y. Dauphin. 2018. “Hierarchical Neural Story Generation”. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 889–98. https://doi.org/10.18653/v1/P18-1082.

Harvard

Fan, A., Lewis, M. and Dauphin, Y. (2018) “Hierarchical Neural Story Generation”, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, pp. 889–898. Available at: https://doi.org/10.18653/v1/P18-1082.

Vancouver

1. Fan A, Lewis M, Dauphin Y (2018) Hierarchical Neural Story Generation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, pp 889–898

BibTeX

@inproceedings{fan-etal-2018-hierarchical,
    title = "Hierarchical Neural Story Generation",
    author = "Fan, Angela  and
      Lewis, Mike  and
      Dauphin, Yann",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1082/",
    doi = "10.18653/v1/P18-1082",
    pages = "889--898"
}

Metadata:ACL Anthology

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Statistic	Value
# Train Stories	272,600
# Test Stories	15,138
# Validation Stories	15,620
# Prompt Words	7.7M
# Story Words	200M
Average Length of Prompts	28.4 words
Average Length of Stories	734.5 words

Model Configuration	Valid Perplexity	Test Perplexity
Conv seq2seq	45.27	45.54
+ self-attention	42.01	42.32
+ multihead	40.12	40.39
+ multiscale	38.76	38.91
+ gating	37.37	37.94

Hierarchical Neural Story Generation

Abstract

Topics

Keywords

Table of Contents

Knowls

Knowl 1 — Hierarchical Story Generation Framework

Knowl 2 — Model Fusion for Sequence-to-Sequence Story Generation

Knowl 3 — Gated Multi-Scale Self-Attention for Convolutional Decoders

Knowl 4 — WritingPrompts Story Generation Benchmark Dataset

Knowl 5 — Top-k Random Sampling Generation Strategy

Knowl 6 — Language Modeling Perplexity on WritingPrompts

Knowl 7 — Component Ablation of Gated Multi-Scale Self-Attention

Knowl 8 — Human and Automatic Evaluation of Story Adherence and Hierarchical Generation

Knowl 9 — Text Novelty and Story Diversity vs. Retrieval Baselines

Knowl 10 — Limitations of Hierarchical Story Generation and Sampling

References

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