  The premises of an argument give evidence or other reasons to support a
conclusion. However, the amount of support required depends on the generality
of a conclusion, the nature of the individual premises, and similar. An
argument whose premises make its conclusion rationally worthy to be drawn is
called sufficient in argument quality research. Previous work tackled
sufficiency assessment as a standard text classification problem, not modeling
the inherent relation of premises and conclusion. In this paper, we hypothesize
that the conclusion of a sufficient argument can be generated from its
premises. To study this hypothesis, we explore the potential of assessing
sufficiency based on the output of large-scale pre-trained language models. Our
best model variant achieves an F1-score of .885, outperforming the previous
state-of-the-art and being on par with human experts. While manual evaluation
reveals the quality of the generated conclusions, their impact remains low
ultimately.

Computation and Language