Sequential sentence classification aims to classify each sentence in the document based on the context in which sentences appear. Most existing work addresses this problem using a hierarchical sequence labeling network. However, they ignore considering the latent segment structure of the document, in which contiguous sentences often have coherent semantics. In this paper, we proposed a span-based dynamic local attention model that could explicitly capture the structural information by the proposed supervised dynamic local attention. We further introduce an auxiliary task called span-based classification to explore the span-level representations. Extensive experiments show that our model achieves better or competitive performance against state-of-the-art baselines on two benchmark datasets.