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Datasets:
hminjeong
/
TripleSumm-MoSu

Tasks:
Summarization
Modalities:
Video
Languages:
English
Size:
10K<n<100K
ArXiv:
Tags:
video
summarization
License:
Dataset card Files
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Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 49, in _split_generators
                  import h5py
              ModuleNotFoundError: No module named 'h5py'
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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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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