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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

Split Examples
'test' 11,490
'train' 287,113
'validation' 13,368
@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}
}

cnn_dailymail/plain_text (default config)

  • Config description: Plain text
  • Dataset size: 1.27 GiB
  • Features:
FeaturesDict({
    'article': Text(shape=(), dtype=tf.string),
    'highlights': Text(shape=(), dtype=tf.string),
})

cnn_dailymail/bytes

  • Config description: Uses byte-level text encoding with tfds.features.text.ByteTextEncoder
  • Dataset size: 1.28 GiB
  • Features:
FeaturesDict({
    'article': Text(shape=(None,), dtype=tf.int64, encoder=<ByteTextEncoder vocab_size=257>),
    'highlights': Text(shape=(None,), dtype=tf.int64, encoder=<ByteTextEncoder vocab_size=257>),
})

cnn_dailymail/subwords32k

  • Config description: Uses tfds.features.text.SubwordTextEncoder with 32k vocab size
  • Dataset size: 490.99 MiB
  • Features:
FeaturesDict({
    'article': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=32908>),
    'highlights': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=32908>),
})