Dataset Open Access

Webis-TLDR-17 Corpus

Syed, Shahbaz; Voelske, Michael; Potthast, Martin; Stein, Benno

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Syed, Shahbaz</dc:creator>
  <dc:creator>Voelske, Michael</dc:creator>
  <dc:creator>Potthast, Martin</dc:creator>
  <dc:creator>Stein, Benno</dc:creator>
  <dc:description>This corpus contains preprocessed posts from the Reddit dataset, suitable for abstractive summarization using deep learning. The format is a json file where each line is a JSON object representing a post. The schema of each post is shown below:

	author: string (nullable = true)
	body: string (nullable = true)
	normalizedBody: string (nullable = true)
	content: string (nullable = true)
	content_len: long (nullable = true)
	summary: string (nullable = true)
	summary_len: long (nullable = true)
	id: string (nullable = true)
	subreddit: string (nullable = true)
	subreddit_id: string (nullable = true)
	title: string (nullable = true)

Specifically, the content and summary fields can be directly used as inputs to a deep learning model (e.g. Sequence to Sequence model ). The dataset consists of 3,848,330 posts with an average length of 270 words for content, and 28 words for the summary. The dataset is a combination of both the Submissions and Comments merged on the common schema. As a result, most of the comments which do not belong to any submission have null as their title.

Note : This corpus does not contain a separate test set. Thus it is up to the users to divide the corpus into appropriate training, validation and test sets.

  <dc:subject>Abstractive Summarization</dc:subject>
  <dc:subject>Social Media Dataset</dc:subject>
  <dc:title>Webis-TLDR-17 Corpus</dc:title>
All versions This version
Views 1,6961,698
Downloads 2,4312,431
Data volume 7.6 TB7.6 TB
Unique views 1,5291,531
Unique downloads 1,9981,998


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