Articles
| Open Access | CLUSTERED KNOWLEDGE: ENHANCING MULTI-DOCUMENT ARABIC TEXT SUMMARIZATION THROUGH KEYPHRASE EXTRACTION
Hamzah Omar , Center for AI Technology, Faculty of Information Science and Technology, University Kebangsaan Malaysia (UKM), Bangi, Selangor, MalaysiaAbstract
Multi-document text summarization is a critical task in information retrieval and natural language processing, aimed at distilling essential information from a collection of documents. Arabic, a complex and rich language, presents unique challenges for this task due to its morphology and syntax. In this paper, we propose a novel approach called "Clustered Knowledge" to enhance multi-document Arabic text summarization. Our approach leverages keyphrase extraction techniques to identify and cluster essential concepts from the input documents, facilitating the generation of coherent and informative summaries. We present experimental results demonstrating the effectiveness of our method on a diverse set of Arabic corpora, outperforming existing summarization techniques. "Clustered Knowledge" not only improves the quality of Arabic text summaries but also contributes to better content organization, making it a valuable tool for information retrieval in Arabic content-rich environments.
Keywords
Multi-Document Summarization, Arabic Text Summarization, Keyphrase Extraction
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