Arabic language processing presents significant challenges due to its complex linguistic
patterns and shortage of resources. This study describes MSG-ATS, a new technique to abstractive text
summarization in Arabic that aims to overcome these issues. The key challenge is producing coherent
and high-quality summaries given the Arabic language’s rich syntactic, semantic, and contextual elements.
Traditional approaches, such as word2vec, frequently fail to capture these subtleties well. MSG-ATS uses
multilevel semantic graphs and deep learning techniques to create a more thorough representation of Arabic
text. This approach improves traditional text generation and embedding approaches by collecting syntactic,
semantic, and contextual information fully. MSG-ATS uses a deep neural network to create high-quality
summaries that are coherent and contextually appropriate. To verify MSG-ATS, we performed rigorous
assessments that compared its performance to word2vec, a fundamental word embedding approach. These
assessments employed a unique dataset created expressly for this study and included automated assessment
using the ROUGE measure. The results are compelling: MSG-ATS outperformed the baseline model by
42.4% in precision, 23.8% in recall, and 38.3% overall. The outcomes of this study highlight MSG-ATS’s
potential to considerably increase Arabic text summarization by providing a strong framework that solves
the constraints of existing models while also laying the groundwork for future developments in the area. |
