  Acronyms are the short forms of longer phrases and they are frequently used
in writing, especially scholarly writing, to save space and facilitate the
communication of information. As such, every text understanding tool should be
capable of recognizing acronyms in text (i.e., acronym identification) and also
finding their correct meaning (i.e., acronym disambiguation). As most of the
prior works on these tasks are restricted to the biomedical domain and use
unsupervised methods or models trained on limited datasets, they fail to
perform well for scientific document understanding. To push forward research in
this direction, we have organized two shared task for acronym identification
and acronym disambiguation in scientific documents, named AI@SDU and AD@SDU,
respectively. The two shared tasks have attracted 52 and 43 participants,
respectively. While the submitted systems make substantial improvements
compared to the existing baselines, there are still far from the human-level
performance. This paper reviews the two shared tasks and the prominent
participating systems for each of them.
