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ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
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Dataset For Generating Tl;Dr

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

Dataset For Generating Tl;Dr

Abstract

This is the dataset for the TL;DR challenge containing posts 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: 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 3,084,410 posts with an average length of 211 words for content, and 25 words for the summary. Note : As this is the complete dataset for the challenge, it is up to the participants to split it into training and validation sets accordingly.

Related Organizations
Keywords

social media, abstractive summarization, tl;dr challenge, user-generated content

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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