|
|
--- |
|
|
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} |
|
|
} |
|
|
``` |
