cnn_dailymail

  • Description:

CNN/DailyMail non-anonymized summarization dataset.

There are two features:

  • article: text of news article, used as the document to be summarized
  • highlights: joined text of highlights with and around each highlight, which is the target summary

  • Homepage: https://github.com/abisee/cnn-dailymail

  • Source code: tfds.summarization.CnnDailymail

  • Versions:

    • 1.0.0: New split API (https://tensorflow.org/datasets/splits)
    • 2.0.0: Separate target sentences with newline. (Having the model predict newline separators makes it easier to evaluate using summary-level ROUGE.)

    • 3.0.0: Using cased version.

    • 3.1.0 (default): Removed BuilderConfig

  • Download size: 558.32 MiB

  • Dataset size: 1.27 GiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'test' 11,490
'train' 287,113
'validation' 13,368
  • Features:
FeaturesDict({
    'article': Text(shape=(), dtype=tf.string),
    'highlights': Text(shape=(), dtype=tf.string),
})
@article{DBLP:journals/corr/SeeLM17,
  author    = {Abigail See and
               Peter J. Liu and
               Christopher D. Manning},
  title     = {Get To The Point: Summarization with Pointer-Generator Networks},
  journal   = {CoRR},
  volume    = {abs/1704.04368},
  year      = {2017},
  url       = {http://arxiv.org/abs/1704.04368},
  archivePrefix = {arXiv},
  eprint    = {1704.04368},
  timestamp = {Mon, 13 Aug 2018 16:46:08 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/SeeLM17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{hermann2015teaching,
  title={Teaching machines to read and comprehend},
  author={Hermann, Karl Moritz and Kocisky, Tomas and Grefenstette, Edward and Espeholt, Lasse and Kay, Will and Suleyman, Mustafa and Blunsom, Phil},
  booktitle={Advances in neural information processing systems},
  pages={1693--1701},
  year={2015}
}