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The task of Argument Mining, that is extracting and classifying argumentcomponents for a specific topic from large document sources, is an inherentlydifficult task for machine learning models and humans alike, as large ArgumentMining datasets are rare and recognition of argument components requires expertknowledge. The task becomes even more difficult if it also involves stancedetection of retrieved arguments. In this work, we investigate the effect ofArgument Mining dataset composition in few- and zero-shot settings. Ourfindings show that, while fine-tuning is mandatory to achieve acceptable modelperformance, using carefully composed training samples and reducing thetraining sample size by up to almost 90% can still yield 95% of the maximumperformance. This gain is consistent across three Argument Mining tasks onthree different datasets. We also publish a new dataset for futurebenchmarking.

Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasets