Automatic text summarization is essential for managing information overload, particularly in domains such as law, where documents are lengthy and complex. Existing extractive summarization methods based on metaheuristic algorithms often suffer from poor initialization and single-objective optimization, resulting in redundant and semantically weak summaries. This work presents MarineSumm, a novel extractive summarization framework designed to improve both lexical quality and semantic relevance in long legal texts. MarineSumm enhances the Marine Predator Optimization (MPA) algorithm through three key contributions. First, it replaces random initialization with a PageRank-based strategy that uses sentence centrality to guide the starting population. Second, it incorporates SBERT embeddings to model semantic similarity between sentences more effectively. Third, it introduces a dynamic multi-objective fitness function that adaptively balances ROUGE scores and SBERT-based cosine similarity across iterations, optimizing for both surface-level relevance and deeper semantic alignment. To further refine candidate solutions, MarineSumm applies a controlled Lévy-flight mutation, sigmoid-based binarized encoding, and sentence-length constraints, which improve search diversity and ensure concise, coherent outputs. Evaluated on the BillSum dataset, MarineSumm achieves ROUGE-1 of 0.5348, ROUGE-2 of 0.2547, and ROUGE-L of 0.3344, outperforming standard metaheuristic baselines including Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization. These results demonstrate the effectiveness of integrating graph-based initialization, semantic-aware scoring, and adaptive optimization into the MPA framework. MarineSumm offers a robust, unsupervised solution for summarizing legal and technical documents and can serve as a reliable extractive component within hybrid summarization pipelines.