README.md

# MITFLD: MIT Lecture Fragmentation Dataset

MITFLD is a **lecture video fragmentation dataset** introduced in [_Towards Key Point Identification (KPI) for Lecture Videos: Approaches and Performance Evaluation_](https://doi.org/10.1145/3746640), serving as a **benchmark for lecture video fragmentation methods** without using synthetic videos.

It enables research in:

- **Key Point Identification (KPI) in lecture videos**
- Lecture fragment recommendation
- Non-linear and bite-sized learning
- Content-based indexing and search for lecture videos

## Data Source Attribution

The MITFLD dataset is **sourced from [MIT OpenCourseWare](https://ocw.mit.edu/)**, a free and open publication of material from thousands of MIT courses, enabling global access to high-quality educational resources.

## Folder Structure

```
├── frags
│   └── <video_id>.json         # Ground truth fragment annotations
├── README.md
├── transcripts
│   └── <video_id>.json         # Transcript of the lecture
├── video_id_list.txt           # List of video_ids included
└── videos
    └── <video_id>.mp4          # Lecture video files
```

## KPI Framework

To facilitate experiments, the dataset is designed to work seamlessly with the **[kpi](https://bit.ly/kpi_lecture_frag) unified Python framework** for lecture video fragmentation, providing:

- Dataset abstractions
- Multiple baseline and advanced methods (e.g., BiLSTM, TW-FINCH, PSD)
- Evaluation metrics (F-score, mMoF, mIoU)
- Extensibility for your own methods

## Citation

If you use this dataset or the `kpi` framework in your research, please cite:

```bibtex
@article{wang2025towards,
  title={Towards Key Point Identification (KPI) for Lecture Videos: Approaches and Performance Evaluation},
  author={Wang, Jiaqi and Kwok, Ricky Y-K and Ngai, Edith CH},
  journal={ACM Transactions on Multimedia Computing, Communications and Applications},
  year={2025},
  publisher={ACM New York, NY}
}
```