Dataset Open Access

Webis-TLDR-17 Corpus

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

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  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.1043504", 
  "language": "eng", 
  "title": "Webis-TLDR-17 Corpus", 
  "issued": {
    "date-parts": [
  "abstract": "<p>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:</p>\n\n<ul>\n\t<li>author: string (nullable = true)</li>\n\t<li>body: string (nullable = true)</li>\n\t<li>normalizedBody: string (nullable = true)</li>\n\t<li>content: string (nullable = true)</li>\n\t<li>content_len: long (nullable = true)</li>\n\t<li>summary: string (nullable = true)</li>\n\t<li>summary_len: long (nullable = true)</li>\n\t<li>id: string (nullable = true)</li>\n\t<li>subreddit: string (nullable = true)</li>\n\t<li>subreddit_id: string (nullable = true)</li>\n\t<li>title: string (nullable = true)</li>\n</ul>\n\n<p>Specifically, the <strong>content</strong> and <strong>summary</strong> 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 <strong>null</strong> as their title.</p>\n\n<p><strong>Note :&nbsp;</strong>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.<br>\n<br>\n&nbsp;</p>", 
  "author": [
      "family": "Syed, Shahbaz"
      "family": "Voelske, Michael"
      "family": "Potthast, Martin"
      "family": "Stein, Benno"
  "id": "1043504", 
  "type": "dataset", 
  "event": "EMNLP 2017 Workshop on New Frontiers in Summarization (EMNLP 2017)"
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