Year: 2023
Type: conference-paper
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 largescale 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.
Institutions: Microsoft (United States), Virginia Tech
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4389518351