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README.md

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-license: cc-by-nc-sa-4.0
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+# 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}
+}
+```
+

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