NewsTent
NewsTent extracts 8,443,484 articles and their summaries from 22 newspapers of 19 Arab countries
dated from 1999 to 2019
Multiple institutions
NewsTent: Arabic Text Summarization Dataset of 8
Million Arabic News Articles with Summaries

summarization
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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.