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

• Additional Documentation: Explore on Papers With Code

• 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: Removed BuilderConfig

  • 3.2.0: Remove extra space before added sentence period. This shouldn't affect ROUGE scores because punctuation is removed.

  • 3.3.0: Add publisher feature.

  • 3.4.0 (default): Add ID feature.

• Download size: 558.32 MiB

• Dataset size: 1.29 GiB

• Auto-cached (documentation): No

• Splits:

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

• Feature structure:

FeaturesDict({
    'article': Text(shape=(), dtype=string),
    'highlights': Text(shape=(), dtype=string),
    'id': Text(shape=(), dtype=string),
    'publisher': Text(shape=(), dtype=string),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
article	Text		string
highlights	Text		string
id	Text		string
publisher	Text		string

• Supervised keys (See as_supervised doc): ('article', 'highlights')

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@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}
}