reddit

• Description:

This corpus contains preprocessed posts from the Reddit dataset. The dataset consists of 3,848,330 posts with an average length of 270 words for content, and 28 words for the summary.

Features includes strings: author, body, normalizedBody, content, summary, subreddit, subreddit_id. Content is used as document and summary is used as summary.

• Additional Documentation: Explore on Papers With Code

• Homepage: https://github.com/webis-de/webis-tldr-17-corpus

• Source code: tfds.datasets.reddit.Builder

• Versions:

  • 1.0.0 (default): No release notes.

• Download size: 2.93 GiB

• Dataset size: 18.09 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'train'	3,848,330

• Feature structure:

FeaturesDict({
    'author': string,
    'body': string,
    'content': string,
    'id': string,
    'normalizedBody': string,
    'subreddit': string,
    'subreddit_id': string,
    'summary': string,
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
author	Tensor		string
body	Tensor		string
content	Tensor		string
id	Tensor		string
normalizedBody	Tensor		string
subreddit	Tensor		string
subreddit_id	Tensor		string
summary	Tensor		string

• Supervised keys (See as_supervised doc): ('content', 'summary')

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@inproceedings{volske-etal-2017-tl,
    title = "{TL};{DR}: Mining {R}eddit to Learn Automatic Summarization",
    author = {V{\"o}lske, Michael  and
      Potthast, Martin  and
      Syed, Shahbaz  and
      Stein, Benno},
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4508",
    doi = "10.18653/v1/W17-4508",
    pages = "59--63",
    abstract = "Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.",
}