Text summarization has been intensively studied in many languages, and some
languages have reached advanced stages. Yet, Arabic Text Summarization (ATS) is
still in its developing stages. Existing ATS datasets are either small or lack
diversity. We build, LANS, a large-scale and diverse dataset for Arabic Text
Summarization task. LANS offers 8.4 million articles and their summaries
extracted from newspapers websites metadata between 1999 and 2019. The
high-quality and diverse summaries are written by journalists from 22 major
Arab newspapers, and include an eclectic mix of at least more than 7 topics
from each source. We conduct an intrinsic evaluation on LANS by both automatic
and human evaluations. Human evaluation of 1000 random samples reports 95.4%
accuracy for our collected summaries, and automatic evaluation quantifies the
diversity and abstractness of the summaries. The dataset is publicly available
upon request.

我们建立了一个大型、多样性的阿拉伯文本摘要数据集 LANS，该数据集为 1999 年至 2019 年期间从报纸网站元数据中提取的 840 万篇文章及其摘要。该数据集包含来自 22 家主要阿拉伯报纸的多样化和高质量的摘要，每个来源至少涵盖 7 个以上的主题。自动和人工评估均表明，我们的摘要准确度高达 95.4％，并且具有摘要的多样性和抽象性。该数据集可应要求公开使用。