Multiple instance learning (MIL) stands as a powerful approach in weakly supervised learning, regularly employed in histological whole slide image (WSI) classification for detecting lesions. However, existing MIL mainstream methods focus on modeling correlation between instances while overlooking the inherent diversity among instances. Meanwhile, few MIL methods aimed at diversity modeling have emerged, showing a performance gap with mainstream MIL methods and facing diversity limits due to computational constraints. To bridge this gap, we propose a novel MIL aggregation method based on diverse global representation (DGR-MIL), by modeling diversity among instances through a set of global vectors. As a result, the global vectors serve as a summary of all instances. First, we turn the instance correlation into the similarity between instance embeddings and the predefined global vectors through a cross-attention mechanism. This stems from the fact that similar instance embeddings typically would result in a higher correlation with a certain global vector. Second, we propose two mechanisms to enforce the diversity among the global vectors to be more descriptive of the entire bag: (i) positive instance alignment and (ii) a novel, efficient, and theoretically guaranteed diversification learning. Specifically, the positive instance alignment module encourages the global vectors to align with instances of interest center (e.g., tumor WSI/bag). To further diversify the global representations, we propose a novel diversity loss. The proposed model outperforms the state-of-the-art MIL aggregation models by a substantial margin on the CAMELYON-16 and the TCGA-lung cancer datasets. The source code will be released upon acceptance.