---
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- summarization
- video-text-to-text
---

# LearningPaper24 Dataset

This dataset contains video recordings and metadata from ICLR and NeurIPS 2024 conference talks. It includes both poster and oral presentations, along with their associated metadata such as titles, abstracts, keywords, and primary areas.

## Dataset Structure

```
learningpaper24/
├── README.md
├── metadata/
│   └── catalog.json
└── video/
    ├── {openreview_id}_{slideslive_id}.mp4
    └── ...
```

## Data Format

### Catalog (metadata/catalog.json)
The catalog contains metadata for each talk in JSON format with the following fields:
- `video_file`: Filename of the video recording in the format `{openreview_id}_{slideslive_id}.mp4`
- `openreview_id`: Unique identifier from OpenReview
- `slideslive_id`: Video identifier from SlidesLive
- `venue`: Conference venue (e.g., "iclr2024")
- `title`: Paper title
- `status`: Presentation type (e.g., "Poster", "Oral")
- `keywords`: Research keywords
- `tldr`: Short summary
- `abstract`: Full paper abstract
- `primary_area`: Main research area
- `site`: Link to the conference page

### Videos
Videos are stored in the `video` directory with filenames following the format: `{openreview_id}_{slideslive_id}.mp4`

## Usage
For easy access to the dataset, you can use the HuggingFace Datasets library. Here's a simple example to load and explore the dataset:
```python
from datasets import load_dataset
dataset = load_dataset("vivianchen98/LearningPaper24", data_files="metadata/catalog.jsonl", split="train")

for i in range(3):
    print(f"\nSAMPLE {i + 1}")
    print("-" * 30)
    print(f"Video file: video/{dataset[i].get('video_file', 'N/A')}")
    print(f"TL;DR: {dataset[i].get('tldr', 'N/A')}")
    print(f"Primary area: {dataset[i].get('primary_area', 'N/A')}")
    print("=" * 50)
```

You can also use the example code provided in `example.py` for more systematic data exploration.

## Purpose

This dataset can be used for:
- Video understanding and summarization
- Natural language processing tasks
- Video-text alignment studies

## Dataset Statistics

The LearningPaper24 dataset contains a diverse collection of machine learning conference presentations:

### Overview
- 📊 **Total entries**: 2,287 conference talks

### Presentation Types
- 📝 **Poster presentations**: 1,986 (86.8%)
- 🔍 **Spotlight presentations**: 256 (11.2%)
- 🎤 **Oral presentations**: 45 (2.0%)

### Conference Distribution
- 🏢 **NeurIPS 2024**: 1,726 talks (75.5%)
- 🏢 **ICLR 2024**: 561 talks (24.5%)

### Research Areas
The dataset covers a wide range of machine learning topics, with the top 10 research areas being:

| Research Area | Count | Percentage |
|---------------|-------|------------|
| Machine Vision | 218 | 9.5% |
| Reinforcement Learning | 125 | 5.5% |
| Natural Language Processing | 123 | 5.4% |
| Optimization | 112 | 4.9% |
| Learning Theory | 111 | 4.9% |
| Diffusion-based Models | 87 | 3.8% |
| Deep Learning Architectures | 79 | 3.5% |
| Generative Models | 79 | 3.5% |
| Probabilistic Methods | 74 | 3.2% |
| Generative Models | 74 | 3.2% |


## License

This dataset is released under the [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/).

<!-- ## Citation

If you use this dataset, please cite:
```
@misc{learningpaper24,
  author = {[Your Name]},
  title = {LearningPaper24: A Dataset of ICLR and NeurIPS 2024 Conference Talks},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
  howpublished = {\url{https://huggingface.co/datasets/[username]/learningpaper24}}
}
``` -->