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 1,000 random samples reports 95.4% accuracy for our collected summaries, and automatic evaluation quantifies the diversity and abstractness of the summaries.

This paper describes the architecture of a novel Multi-Layer Long Text Summarizer (MLLTS) system proposed for the task of creative writing summarization. Typically, such writings are very long, often spanning over 100 pages. Summarizers available online are either not equipped enough to handle long texts, or even if they are able to generate the summary, the quality is poor. The proposed MLLTS system handles the difficulty by splitting the text into several parts. Each part is then subjected to different existing summarizers. A multilayer network is constructed by establishing linkages between the different parts. During training phases, several hyperparameters are fine-tuned. The system achieved very good ROUGE scores on the test data supplied for the contest.