Datasets:
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---
task_categories:
- image-text-to-text
license: other
dataset_info:
features:
- name: filename
dtype: string
- name: size
dtype: int64
- name: url
dtype: string
- name: license
dtype: string
- name: title
dtype: string
- name: created
dtype: string
- name: updated
dtype: string
- name: doi
dtype: string
- name: checksum
dtype: string
splits:
- name: pptx
num_bytes: 3925161
num_examples: 10448
download_size: 2028492
dataset_size: 3925161
configs:
- config_name: default
data_files:
- split: pptx
path: data/pptx-*
---
# PPTAgent/Zenodo10K
This is the dataset used in [PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides](https://arxiv.org/abs/2501.03936) and also used for evaluation in [VLM-SlideEval: Evaluating VLMs on Structured Comprehension and Perturbation Sensitivity in PPT](https://huggingface.co/papers/2510.22045). It was crawled from [zenodo](http://zenodo.org).
To the best of our knowledge, it is the **largest presentation dataset** currently available, comprising over 10,000 **PowerPoint (.pptx)** files, all distributed under a clear and compliant license.
For more information regarding the `PPTAgent` project, please visit its [github repo](https://github.com/icip-cas/PPTAgent).
## VLM-SlideEval Abstract
Vision-language models (VLMs) are increasingly used to evaluate multimodal content, including presentation slides, yet their slide-specific understanding remains underexplored {despite their growing role as critics in agentic, model-forward pipelines}. We introduce VLM-SlideEval, an evaluation framework that probes VLMs along three axes: (1) element-level extraction from slide images aligned to ground truth; (2) robustness to controlled perturbations in geometry, style, and text; and (3) higher-level comprehension, such as recovering a deck's narrative order from shuffled slides. Using publicly available decks from Zenodo ( this https URL ), we standardize ground-truth element metadata from PowerPoint XML and live renderings into a unified, verifiable schema. Empirically, VLMs underperform on pixel-accurate extraction and show non-trivial agreement, fidelity, and consistency under controlled perturbations, while performing better on single-slide content understanding; however, they do not reliably capture narrative structure across slides. These results highlight the limits of current VLMs for slide evaluation and motivate calibrated, critic-in-the-loop evaluators that drive iterative refinement and selection in agentic pipelines.
## Sample Usage
```python
dirname = f"zenodo-pptx/pptx/{task['license']}/{task['created'][:4]}/"
basename = f"{task['checksum'][4:]}-{task['filename']}"
filepath = dirname + basename
try:
open('/tmp/'+basename,'wb').close()
except:
filepath = dirname + basename[:240] + ".pptx"
```
## Citation
If you find this project helpful, please use the following to cite it:
```bibtex
@misc{zheng2025pptagentgeneratingevaluatingpresentations,
title={PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides},
author={Hao Zheng and Xinyan Guan and Hao Kong and Jia Zheng and Hongyu Lin and Yaojie Lu and Ben He and Xianpei Han and Le Sun},
year={2025},
eprint={2501.03936},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2501.03936},
}
```
If you use this dataset for VLM evaluation, please also cite:
```bibtex
@article{xu2025vlm,
title={VLM-SlideEval: Evaluating VLMs on Structured Comprehension and Perturbation Sensitivity in PPT},
author={Xu, Charles and Li, Qiyang and Luo, Jianlan and Levine, Sergey},
journal={arXiv preprint arXiv:2510.22045},
year={2025}
}
``` |