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MSG-ATS: Multi-Level Semantic Graph for Arabic Text Summarization
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DOI：10.1109/ACCESS.2024.3441489.png)
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Abstract
En 中文
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.
Keywords:
Semantics
Text summarization
Measurement
Syntactics
Reviews
Long short term memory
Deep learning
Automation
Graph neural networks
Automatic text summarization
multi-level semantic graph
semantic graph embedding
graph neural networks
attention mechanisms
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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