  Meetings are increasingly important for collaborations. Action items in
meeting transcripts are crucial for managing post-meeting to-do tasks, which
usually are summarized laboriously. The Action Item Detection task aims to
automatically detect meeting content associated with action items. However,
datasets manually annotated with action item detection labels are scarce and in
small scale. We construct and release the first Chinese meeting corpus with
manual action item annotations. In addition, we propose a Context-Drop approach
to utilize both local and global contexts by contrastive learning, and achieve
better accuracy and robustness for action item detection. We also propose a
Lightweight Model Ensemble method to exploit different pre-trained models.
Experimental results on our Chinese meeting corpus and the English AMI corpus
demonstrate the effectiveness of the proposed approaches.
