We approach aspect-based argument mining as a supervised machine learning task to classify arguments into semantically coherent groups referring to the same defined aspect categories. As an exemplary use case, we introduce the Argument Aspect Corpus - Nuclear Energy that separates arguments about the topic of nuclear energy into nine major aspects. Since the collection of training data for further aspects and topics is costly, we investigate the potential for current transformer-based few-shot learning approaches to accurately classify argument aspects. The best approach is applied to a British newspaper corpus covering the debate on nuclear energy over the past 21 years. Our evaluation shows that a stable prediction of shares of argument aspects in this debate is feasible with 50 to 100 training samples per aspect. Moreover, we see signals for a clear shift in the public discourse in favor of nuclear energy in recent years. This revelation of changing patterns of pro and contra arguments related to certain aspects over time demonstrates the potential of supervised argument aspect detection for tracking issue-specific media discourses.

Aspect-based argument mining (ABAM) is the task of automatic _detection_ and _categorization_ of argument aspects, i.e. the parts of an argumentative text that contain the issue-specific key rationale for its conclusion. From empirical data, overlapping but not congruent sets of aspect categories can be derived for different topics. So far, two supervised approaches to detect aspect boundaries, and a smaller number of unsupervised clustering approaches to categorize groups of similar aspects have been proposed. With this paper, we introduce the Argument Aspect Corpus (AAC) that contains token-level annotations of aspects in 3,547 argumentative sentences from three highly debated topics. This dataset enables both the supervised learning of boundaries and categorization of argument aspects. During the design of our annotation process, we noticed that it is not clear from the outset at which contextual unit aspects should be coded. We, thus, experiment with classification at the token, chunk, and sentence level granularity. Our finding is that the chunk level provides the most useful information for applications. At the same time, it produces the best performing results in our tested supervised learning setups.