natural-language-processing-8

Story Generation

3260 papers • 126 benchmarks • 313 datasets

Story generation is the task of automatically generating a coherent narrative, often from a set of premises or a brief summary.

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Most implemented papers

Hierarchical Neural Story Generation

Yann Dauphin, M. Lewis, Angela Fan•Mon Apr 30 2018

This work collects a large dataset of 300K human-written stories paired with writing prompts from an online forum that enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text.

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A Temporal Variational Model for Story Generation

Frank Keller, David Wilmot•Mon Sep 13 2021

Experiments with a latent vector planning approach based on a TD-VAE (Temporal Difference Variational Autoencoder) using the model for conditioning and reranking for text generation demonstrate strong performance in automatic cloze and swapping evaluations.

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GLAC Net: GLocal Attention Cascading Networks for Multi-image Cued Story Generation

Byoung-Tak Zhang, Taehyeong Kim, Min-Oh Heo, Seonil Son, Kyoung-Wha Park•Sun May 27 2018

A deep learning network model, GLAC Net, is proposed that generates visual stories by combining global-local (glocal) attention and context cascading mechanisms and achieves very competitive results compared to the state-of-the-art techniques.

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Plan-And-Write: Towards Better Automatic Storytelling

Dongyan Zhao, Nanyun Peng, Rui Yan, Kevin Knight, R. Weischedel, Lili Yao•Tue Nov 13 2018

Experiments show that with explicit storyline planning, the generated stories are more diverse, coherent, and on topic than those generated without creating a full plan, according to both automatic and human evaluations.

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PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking

Jianfeng Gao, Asli Celikyilmaz, Yejin Choi, Hannah Rashkin•Wed Apr 29 2020

PlotMachines is presented, a neural narrative model that learns to transform an outline into a coherent story by tracking the dynamic plot states, and is enriched with high-level discourse structure so that the model can learn different styles of writing corresponding to different parts of the narrative.

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On Faithfulness and Factuality in Abstractive Summarization

Shashi Narayan, Ryan T. McDonald, Bernd Bohnet, Joshua Maynez•Fri May 01 2020

It is found that neural abstractive summarization models are highly prone to hallucinate content that is unfaithful to the input document and textual entailment measures better correlate with faithfulness than standard metrics, potentially leading the way to automatic evaluation metrics as well as training and decoding criteria.

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Transformer-based Conditional Variational Autoencoder for Controllable Story Generation

Changyou Chen, Liefeng Bo, Le Fang, Tao Zeng, Chao-Ning Liu, Wen Dong•Sun Jan 03 2021

This paper integrates latent representation vectors with a Transformer-based pre-trained architecture to build conditional variational autoencoder (CVAE), and demonstrates state-of-the-art conditional generation ability of the model, as well as its excellent representation learning capability and controllability.

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Language Models Can See: Plugging Visual Controls in Text Generation

Dani Yogatama, Nigel Collier, Fangyu Liu, Tian Lan, Yan Wang, Yixuan Su, Lingpeng Kong, Yahui Liu•Wed May 04 2022

A training-free framework for plugging in visual controls in the generation process and enabling LMs to perform multimodal tasks (e.g., image captioning) in a zero-shot manner, which outperforms the state-of-the-art method by notable margins with a nearly 27 times decoding speedup.

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Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation

C. Clavel, Pierre Colombo, Cyril Chhun, Fabian M. Suchanek•Tue Aug 23 2022

This paper introduces a set of 6 orthogonal and comprehensive human criteria, carefully motivated by the social sciences literature, and presents HANNA, an annotated dataset of 1,056 stories produced by 10 different ASG systems, to quantitatively evaluate the correlations of 72 automatic metrics with human criteria.

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Event Representations for Automated Story Generation with Deep Neural Nets

Prithviraj Ammanabrolu, Lara J. Martin, William Hancock, Shruti Singh, Brent Harrison, Mark O. Riedl•Sun Jun 04 2017

The question of event representations that provide a mid-level of abstraction between words and sentences in order to retain the semantic information of the original data while minimizing event sparsity is explored.

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