reddit_tifu

• Description:

Reddit dataset, where TIFU denotes the name of subbreddit /r/tifu. As defined in the publication, styel "short" uses title as summary and "long" uses tldr as summary.

Features includes: - document: post text without tldr. - tldr: tldr line. - title: trimmed title without tldr. - ups: upvotes. - score: score. - num_comments: number of comments. - upvote_ratio: upvote ratio.

• Homepage: https://github.com/ctr4si/MMN

• Source code: tfds.summarization.RedditTifu

• Versions:

  • 1.1.0 (default): No release notes.

• Download size: 639.54 MiB

• Dataset size: Unknown size

• Auto-cached (documentation): Unknown

• Feature structure:

FeaturesDict({
    'documents': Text(shape=(), dtype=tf.string),
    'num_comments': tf.float32,
    'score': tf.float32,
    'title': Text(shape=(), dtype=tf.string),
    'tldr': Text(shape=(), dtype=tf.string),
    'ups': tf.float32,
    'upvote_ratio': tf.float32,
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
documents	Text		tf.string
num_comments	Tensor		tf.float32
score	Tensor		tf.float32
title	Text		tf.string
tldr	Text		tf.string
ups	Tensor		tf.float32
upvote_ratio	Tensor		tf.float32

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe): Missing.

• Citation:

@misc{kim2018abstractive,
    title={Abstractive Summarization of Reddit Posts with Multi-level Memory Networks},
    author={Byeongchang Kim and Hyunwoo Kim and Gunhee Kim},
    year={2018},
    eprint={1811.00783},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

reddit_tifu/short (default config)

• Config description: Using title as summary.

• Splits:

Split	Examples
'train'	79,740

• Supervised keys (See as_supervised doc): ('documents', 'title')

reddit_tifu/long

• Config description: Using TLDR as summary.

• Splits:

Split	Examples
'train'	42,139

• Supervised keys (See as_supervised doc): ('documents', 'tldr')