LANS: Large-scale Arabic News Summarization Corpus

TL;DR

LANS is a large, diverse Arabic news summarization dataset with 8.4 million articles, enabling advanced research in Arabic text summarization through high-quality summaries from major newspapers.

Contribution

The paper introduces LANS, the largest and most diverse Arabic news summarization dataset, addressing limitations of previous datasets in size and diversity.

Findings

High human accuracy (95.4%) in summary quality

Automatic evaluation confirms diversity and abstractness

Dataset covers over 7 topics from 22 newspapers

Abstract

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.