Transformer Models in Education: Summarizing Science Textbooks with AraBART, MT5, AraT5, and mBART

TL;DR

This paper develops and evaluates Arabic text summarization models using modern NLP architectures like MT5, AraBART, AraT5, and mBART50 to assist students and teachers in understanding science textbooks more effectively.

Contribution

It introduces a new Arabic textbook summarization system employing state-of-the-art models and provides a dataset for Arabic educational texts, advancing NLP applications in Arabic education.

Findings

Models achieved high Rouge scores in summarization quality.

Expert evaluations confirmed the usefulness of the summaries.

The system effectively extracts key information from Arabic science textbooks.

Abstract

Recently, with the rapid development in the fields of technology and the increasing amount of text t available on the internet, it has become urgent to develop effective tools for processing and understanding texts in a way that summaries the content without losing the fundamental essence of the information. Given this challenge, we have developed an advanced text summarization system targeting Arabic textbooks. Relying on modern natu-ral language processing models such as MT5, AraBART, AraT5, and mBART50, this system evaluates and extracts the most important sentences found in biology textbooks for the 11th and 12th grades in the Palestinian curriculum, which enables students and teachers to obtain accurate and useful summaries that help them easily understand the content. We utilized the Rouge metric to evaluate the performance of the trained models. Moreover, experts in education Edu textbook authoring assess the output of the trained models. This approach aims to identify the best solutions and clarify areas needing improvement. This research provides a solution for summarizing Arabic text. It enriches the field by offering results that can open new horizons for research and development in the technologies for understanding and generating the Arabic language. Additionally, it contributes to the field with Arabic texts through creating and compiling schoolbook texts and building a dataset.