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Datasets:
hminjeong
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TripleSumm-MoSu

Tasks:
Summarization
Modalities:
Video
Languages:
English
Size:
10K<n<100K
ArXiv:
Tags:
video
summarization
License:
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The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Dataset Summary

MoSu (Most Replayed Multimodal Video Summarization) is the first large-scale multimodal video summarization dataset. It provides synchronized visual, audio, and text features for 52,678 in-the-wild videos. The ground-truth annotations are based on YouTube's "Most Replayed" statistics, offering highly reliable per-frame importance scores derived from collective viewer engagement.

Dataset Structure

The dataset consists of 6 core files providing metadata, multimodal features, ground truth annotations, and evaluation splits.

1. Metadata (mosu_metadata.csv)

Contains the foundational information for all 52,678 videos.

  • video_id: The unique identifier for the video. This serves as the universal key to access data in all .h5 files and the split JSON.
  • youtube_id: The original YouTube video ID. The video can be accessed via https://www.youtube.com/watch?v={youtube_id}.
  • duration: The length of the video in seconds.
  • views: The total view count of the video.
  • labels: Original multi-label annotations provided by the YouTube-8M dataset.
  • cluster_id: One of 10 semantic clusters (0-9). These clusters were generated based on metadata to group videos by topic (e.g., Video Games, Sports) and ensure a balanced distribution across dataset splits. For more details, please refer to the original paper.

2. Multimodal Features (.h5 files)

Pre-extracted features for all three modalities. Each file is provided in HDF5 format and is approximately 40GB in size. All features have a shape of (N, D), where N corresponds to the video's duration (in seconds) indicated in the metadata, and D is 768 for all modalities.

  • mosu_feat_visual_clip.h5: Visual features extracted using CLIP.
  • mosu_feat_audio_ast.h5: Audio features extracted using Audio Spectrogram Transformer (AST).
  • mosu_feat_text_roberta.h5: Text features extracted using RoBERTa.

File Size & Downloading: The feature files are extremely large (~40GB each). Depending on network conditions, downloading may take a considerable amount of time.

3. Ground Truth (mosu_gt.h5)

An HDF5 file containing the summarization labels for all 52,678 videos. Each video_id (e.g., '005O') maps to an HDF5 Group containing four specific keys:

  • change_points: Temporal boundaries for video shots.
  • cluster_id: The semantic cluster ID of the video.
  • gt_score: Frame-level ground-truth importance scores.
  • gt_summary: Binary labels indicating whether a frame is included in the final summary.

4. Dataset Splits (mosu_split.json)

Contains standardized splits for training, validation, and testing. The split ratio strictly maintains the proportional representation of each cluster_id for a balanced evaluation.

  • train_keys: List of video IDs for 42,152 training videos.
  • val_keys: List of video IDs for 5,263 validation videos.
  • test_keys: List of video IDs for 5,263 testing videos.

Citation

If you use the MoSu dataset or the TripleSumm model in your research, please cite the following paper:

@inproceedings{triplesumm2026,
  title={TripleSumm: Adaptive Triple-Modality Fusion for Video Summarization},
  author={Kim, Sumin and Jeong, Hyemin and Kang, Mingu and Kim, Yejin and Oh, Yoori and Lee, Joonseok},
  booktitle={Proceedings of the International Conference on Learning Representations (ICLR)},
  year={2026}
}
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