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  • 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
  • Feature structure:
    '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
article Text string
highlights Text string
id Text string
publisher Text string
  • Citation:
  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       = {},
  archivePrefix = {arXiv},
  eprint    = {1704.04368},
  timestamp = {Mon, 13 Aug 2018 16:46:08 +0200},
  biburl    = {},
  bibsource = {dblp computer science bibliography,}

  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},