  Argument mining is a core technology for automating argument search in large
document collections. Despite its usefulness for this task, most current
approaches to argument mining are designed for use only with specific text
types and fall short when applied to heterogeneous texts. In this paper, we
propose a new sentential annotation scheme that is reliably applicable by crowd
workers to arbitrary Web texts. We source annotations for over 25,000 instances
covering eight controversial topics. The results of cross-topic experiments
show that our attention-based neural network generalizes best to unseen topics
and outperforms vanilla BiLSTM models by 6% in accuracy and 11% in F-score.
