We study a two-level multiview learning with more than two views under the PAC-Bayesian framework. This approach, sometimes referred as late fusion, consists in learning sequentially multiple view-specific classifiers at the first level, and then combining these view-specific classifiers at the second level. Our main theoretical result is a generalization bound on the risk of the majority vote which exhibits a term of diversity in the predictions of the view-specific classifiers. From this result it comes out that controlling the trade-off between diversity and accuracy is a key element for multiview learning, which complements other results in multiview learning. Finally, we experiment our principle on multiview datasets extracted from the Reuters RCV1/RCV2 collection. 1 The fusion of descriptions, resp. of models, is sometimes called Early Fusion, resp. Late Fusion. 2 Our notion of hyper-prior and hyper-posterior distributions is different than the one proposed for lifelong learning [Pentina and Lampert, 2014], where they basically consider hyper-prior and hyper-posterior over the set of possible priors: The prior distribution P over the voters' set is viewed as a random variable.