newsroom

• Description:

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.jsonl, test.jsonl" to the tfds folder.

• Additional Documentation: Explore on Papers With Code north_east

• Homepage: https://summari.es

• Source code: tfds.datasets.newsroom.Builder

• Versions:

  • 1.0.0 (default): No release notes.

• Download size: Unknown size

• Dataset size: 5.13 GiB

• Manual download instructions: This dataset requires you to download the source data manually into download_config.manual_dir (defaults to ~/tensorflow_datasets/downloads/manual/):
  You should download the dataset from https://summari.es/download/ The webpage requires registration. After downloading, please put dev.jsonl, test.jsonl and train.jsonl files in the manual_dir.

• Auto-cached (documentation): No

• Splits:

• Feature structure:

FeaturesDict({
    'compression': float32,
    'compression_bin': Text(shape=(), dtype=string),
    'coverage': float32,
    'coverage_bin': Text(shape=(), dtype=string),
    'date': Text(shape=(), dtype=string),
    'density': float32,
    'density_bin': Text(shape=(), dtype=string),
    'summary': Text(shape=(), dtype=string),
    'text': Text(shape=(), dtype=string),
    'title': Text(shape=(), dtype=string),
    'url': Text(shape=(), dtype=string),
})

• Feature documentation:

• Supervised keys (See as_supervised doc): ('text', 'summary')

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@article{Grusky_2018,
   title={Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies},
   url={http://dx.doi.org/10.18653/v1/n18-1065},
   DOI={10.18653/v1/n18-1065},
   journal={Proceedings of the 2018 Conference of the North American Chapter of
          the Association for Computational Linguistics: Human Language
          Technologies, Volume 1 (Long Papers)},
   publisher={Association for Computational Linguistics},
   author={Grusky, Max and Naaman, Mor and Artzi, Yoav},
   year={2018}
}