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Datasets:
ccdv
/
mediasum

Tasks:
Summarization
Languages:
English
Size:
100K<n<1M
ArXiv:
Tags:
conditional-text-generation
Dataset card Files
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YAML Metadata Warning: The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

MediaSum dataset for summarization

Summarization dataset copied from MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization

This dataset is compatible with the run_summarization.py script from Transformers if you add this line to the summarization_name_mapping variable:

"ccdv/mediasum": ("document", "summary")

Configs

4 possibles configs:

  • roberta will concatenate documents with "</s>"
  • newline will concatenate documents with "\n"
  • bert will concatenate documents with "[SEP]"
  • list will return the list of documents instead of a single string

Add _prepended to config name to prepend the speaker name before each dialogue: speaker: text
Default is roberta_prepended (compatible with BART).

Data Fields

  • id: paper id
  • document: a string/list containing the body of a set of documents
  • summary: a string containing the abstract of the set

Data Splits

This dataset has 3 splits: train, validation, and test. \

Dataset Split Number of Instances
Train 443596
Validation 10000
Test 10000

Cite original article 

@article{zhu2021mediasum,
  title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization},
  author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael},
  journal={arXiv preprint arXiv:2103.06410},
  year={2021}
}
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