  Argument Mining (AM) is a crucial aspect of computational argumentation,
which deals with the identification and extraction of Argumentative Components
(ACs) and their corresponding Argumentative Relations (ARs). Most prior works
have solved these problems by dividing them into multiple subtasks. And the
available end-to-end setups are mostly based on the dependency parsing
approach. This work proposes a unified end-to-end framework based on a
generative paradigm, in which the argumentative structures are framed into
label-augmented text, called Augmented Natural Language (ANL). Additionally, we
explore the role of different types of markers in solving AM tasks. Through
different marker-based fine-tuning strategies, we present an extensive study by
integrating marker knowledge into our generative model. The proposed framework
achieves competitive results to the state-of-the-art (SoTA) model and
outperforms several baselines.
