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Arabic extractive text summarization remains challenging due to rich morphology and limited lexical consistency, which weaken traditional surface-based sentence ranking methods. Existing approaches typically rely either on graph centrality to capture document structure or on transformer-based embeddings to estimate semantic relevance, yet the relationship between these two ranking signals has not been systematically examined. This paper investigates the complementary roles of semantic similarity and structural centrality in Arabic summarization. A controlled hybrid ranking formulation is implemented that combines contextual sentence embeddings with PageRank-based graph scoring and evaluate it using multi-reference ROUGE on the Extended Arabic Summaries Corpus (EASC). The study analyzes how each component influences sentence salience, coverage, and redundancy. Experimental results show that semantic embeddings improve the identification of informative sentences, while graph centrality enhances coverage and reduces repetition; their integration consistently yields stronger summaries, achieving ROUGE-1 = 0.605, ROUGE-2 = 0.497, and ROUGE-L = 0.514. These findings provide empirical evidence that semantic and structural centrality capture complementary aspects of importance in morphologically rich languages, offering guidance for designing more robust extractive summarization systems.