  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.
