# Visual Story Post-Editing

arXiv: https://arxiv.org/abs/1906.01764 · HTML: https://mlsanity.com/paper/1906.01764/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 10 of 10)

- [The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation](https://mlsanity.com/paper/2109.06835/) (2021)
- [Commonsense Knowledge Aware Concept Selection For Diverse and Informative Visual Storytelling](https://mlsanity.com/paper/2102.02963/) (2021)
- [Plot and Rework: Modeling Storylines for Visual Storytelling](https://mlsanity.com/paper/2105.06950/) (2021)
- [Recent Advances in Neural Text Generation: A Task-Agnostic Survey](https://mlsanity.com/paper/2203.03047/) (2022)
- [RoViST:Learning Robust Metrics for Visual Storytelling](https://mlsanity.com/paper/2205.03774/) (2022)
- [SCO-VIST: Social Interaction Commonsense Knowledge-based Visual Storytelling](https://mlsanity.com/paper/2402.00319/) (2024)
- [Knowledge-enriched Attention Network with Group-wise Semantic for Visual Storytelling](https://mlsanity.com/paper/2203.05346/) (2022)
- [The Lost Melody: Empirical Observations on Text-to-Video Generation From A Storytelling Perspective](https://mlsanity.com/paper/2405.08720/) (2024)
- [Story Generation from Visual Inputs: Techniques, Related Tasks, and Challenges](https://mlsanity.com/paper/2406.02748/) (2024)
- [Conveying the Predicted Future to Users: A Case Study of Story Plot Prediction](https://mlsanity.com/paper/2302.09122/) (2023)

## Abstract (arXiv)

We introduce the first dataset for human edits of machine-generated visual stories and explore how these collected edits may be used for the visual story post-editing task. The dataset, VIST-Edit, includes 14,905 human edited versions of 2,981 machine-generated visual stories. The stories were generated by two state-of-the-art visual storytelling models, each aligned to 5 human-edited versions. We establish baselines for the task, showing how a relatively small set of human edits can be leveraged to boost the performance of large visual storytelling models. We also discuss the weak correlation between automatic evaluation scores and human ratings, motivating the need for new automatic metrics.