---
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}
}
```