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, 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, 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 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:
@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}
}