The goal of claim detection in argument mining is to sort out the
key points from a long narrative. In this paper, we design a novel
task for argument mining in the financial domain, and provide an
expert-annotated dataset, NumClaim, for the proposed task. Based
on the statistics, we discuss the differences between the claims
in other datasets and the claims of the investors in NumClaim.
With the ablation analysis, we show that encoding numeral and
co-training with the auxiliary task of the numeral understanding,
i.e., the category classification task, can improve the performance
of the proposed task under different neural network architectures.
The annotations in the NumClaim is published for academic usage
under the CC BY-NC-SA 4.0 license.