Abstract
Argument(ation) mining (AM) is an area of research in Artificial Intelligence (AI) that aims to identify, analyse and automatically generate arguments in natural language. In a pipeline, the identification and analysis of the arguments and their components (i.e. premises and claims) in texts and the prediction of their relations (i.e. attack and support) are then handled by argument-based reasoning frameworks so that, for example, fallacies and inconsistencies can be automatically identified. Recently, the field of argument mining has tackled new challenges, namely the evaluation of argument quality (e.g. strength, persuasiveness), natural language argument summarisation and retrieval, and natural language argument generation. In this paper, I discuss my main contributions in this area as well as some lines of future research. This paper is part of the AAAI-23 New Faculty Highlights.
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