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Dataset: newsroom 🏷
Update on GitHub

How to load this dataset directly with the πŸ€—/datasets library:

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from datasets import load_dataset dataset = load_dataset("newsroom")


NEWSROOM is a large dataset for training and evaluating summarization systems. It contains 1.3 million articles and summaries written by authors and editors in the newsrooms of 38 major publications. Dataset features includes: - text: Input news text. - summary: Summary for the news. And additional features: - title: news title. - url: url of the news. - date: date of the article. - density: extractive density. - coverage: extractive coverage. - compression: compression ratio. - density_bin: low, medium, high. - coverage_bin: extractive, abstractive. - compression_bin: low, medium, high. This dataset can be downloaded upon requests. Unzip all the contents "train.jsonl, dev.josnl, test.jsonl" to the tfds folder.


  author    = {Grusky, Max and Naaman, Mor and Artzi, Yoav},
  title     = {NEWSROOM: A Dataset of 1.3 Million Summaries
               with Diverse Extractive Strategies},
  booktitle = {Proceedings of the 2018 Conference of the
               North American Chapter of the Association for
               Computational Linguistics: Human Language Technologies},
  year      = {2018},

Models trained or fine-tuned on newsroom

None yet. Start fine-tuning now =)