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.

Society, a term that encompasses the intricate web of relationships, institutions, and  cultural norms that shape human interaction, has been a cornerstone of humanity's existence since time immemorial. The concept of society is multifaceted, complex, and dynamic, having evolved over centuries, influenced by diverse factors such as geography, politics, economics, and culture. This article delves into the concept and history of society, exploring its development, transformation, and significance in the human experience