Name: NewsTent
Description: NewsTent extracts 8,443,484 articles and their summaries from 22 newspapers of 19 Arab countries
  dated from 1999 to 2019
Provider: Multiple institutions
Paper Title: NewsTent: Arabic Text Summarization Dataset of 8
  Million Arabic News Articles with Summaries
  
Tasks: summarization
Venue Name: nan
Authors: nan
Abstract: There has been intensive research about text summarization in many languages.
  Some languages have reached advanced stages, but Arabic Text Summarization
  (ATS) is still in its developing stages due to several limitations starting from
  datasets and ending at the evaluation process. Existing ATS datasets usually
  contain a range of 100 to 30,000 training samples which make them too small
  to train a summarization model. Further, existing datasets lack diversity; they
  rely on one source (e.g. newspaper) or a few to collect the summaries, also some
  rely on a single Arab country and ignore the rest. We present, NewsTent, the
  largest and most diverse dataset for Arabic Text Summarization tasks. NewsTent
  overcomes the limitations of existing datasets. First, eight million articles and their
  summaries are collected by the "inverted pyramid" writing style. The summaries
  are verified by human experts and achieved 87.4% accuracy from 1000 samples.
  Second, NewsTent collected news articles from popular newspapers sources of 19
  Arab countries with an eclectic mix of topics varying from finance, politics, sports,
  etc to establish comprehensive diversity. Then, we corroborate the superiority
  of the extracted summaries by comparing them to MT5-generated summaries.
  NewsTent’s large and diverse corpus overcomes the limitations of ATS datasets to
  enhance the ATS field.