Argument mining (AM) is a challenging task as it requires recognizing the complex argumentation structures involving multiple subtasks. To handle all subtasks of AM in an end-to-end fashion, previous works generally transform AM into a dependency parsing task. However, such methods largely require complex pre- and post-processing to realize the task transformation. In this paper, we investigate the end-to-end AM task from a novel perspective by proposing a generative framework, in which the expected outputs of AM are framed as a simple target sequence. Then, we employ a pre-trained sequence-to-sequence language model with a constrained pointer mechanism (CPM) to model the clues for all the subtasks of AM in the light of the target sequence. Furthermore, we devise a reconstructed positional encoding (RPE) to alleviate the order biases induced by the autoregressive generation paradigm. Experimental results show that our proposed framework achieves new state-of-the-art performance on two AM benchmarks.