Dataset Open Access

Dataset for generating TL;DR

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


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  <identifier identifierType="DOI">10.5281/zenodo.1168855</identifier>
  <creators>
    <creator>
      <creatorName>Syed, Shahbaz</creatorName>
      <givenName>Shahbaz</givenName>
      <familyName>Syed</familyName>
      <affiliation>Bauhaus-Universität Weimar</affiliation>
    </creator>
    <creator>
      <creatorName>Voelske, Michael</creatorName>
      <givenName>Michael</givenName>
      <familyName>Voelske</familyName>
      <affiliation>Bauhaus-Universität Weimar</affiliation>
    </creator>
    <creator>
      <creatorName>Potthast, Martin</creatorName>
      <givenName>Martin</givenName>
      <familyName>Potthast</familyName>
      <affiliation>Bauhaus-Universität Weimar</affiliation>
    </creator>
    <creator>
      <creatorName>Stein, Benno</creatorName>
      <givenName>Benno</givenName>
      <familyName>Stein</familyName>
      <affiliation>Bauhaus-Universität Weimar</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Dataset for generating TL;DR</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>tl;dr challenge</subject>
    <subject>abstractive summarization</subject>
    <subject>social media</subject>
    <subject>user-generated content</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-02-08</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1168855</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="Cites">10.5281/zenodo.1043504</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1168854</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This is the dataset for the TL;DR challenge containing posts&amp;nbsp;from the Reddit corpus, 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:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;author: string (nullable = true)&lt;/li&gt;
	&lt;li&gt;body: string (nullable = true)&lt;/li&gt;
	&lt;li&gt;normalizedBody: string (nullable = true)&lt;/li&gt;
	&lt;li&gt;content: string (nullable = true)&lt;/li&gt;
	&lt;li&gt;content_len: long (nullable = true)&lt;/li&gt;
	&lt;li&gt;summary: string (nullable = true)&lt;/li&gt;
	&lt;li&gt;summary_len: long (nullable = true)&lt;/li&gt;
	&lt;li&gt;id: string (nullable = true)&lt;/li&gt;
	&lt;li&gt;subreddit: string (nullable = true)&lt;/li&gt;
	&lt;li&gt;subreddit_id: string (nullable = true)&lt;/li&gt;
	&lt;li&gt;title: string (nullable = true)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Specifically, the &lt;strong&gt;content&lt;/strong&gt; and &lt;strong&gt;summary&lt;/strong&gt; fields can be directly used as inputs to a deep learning model (e.g. Sequence to Sequence model ). The dataset consists of 3,084,410 posts with an average length of 211 words for content, and 25&amp;nbsp;words for the summary.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Note :&amp;nbsp;&lt;/strong&gt;As this is the complete dataset for the challenge, it is up to the participants to split it into training and validation sets accordingly.&lt;/p&gt;</description>
  </descriptions>
</resource>
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