  A crucial component in the curation of KB for a scientific domain (e.g.,
materials science, foods & nutrition, fuels) is information extraction from
tables in the domain's published research articles. To facilitate research in
this direction, we define a novel NLP task of extracting compositions of
materials (e.g., glasses) from tables in materials science papers. The task
involves solving several challenges in concert, such as tables that mention
compositions have highly varying structures; text in captions and full paper
needs to be incorporated along with data in tables; and regular languages for
numbers, chemical compounds and composition expressions must be integrated into
the model. We release a training dataset comprising 4,408 distantly supervised
tables, along with 1,475 manually annotated dev and test tables. We also
present a strong baseline DISCOMAT, that combines multiple graph neural
networks with several task-specific regular expressions, features, and
constraints. We show that DISCOMAT outperforms recent table processing
architectures by significant margins.
