  Numerical tables are widely used to present experimental results in
scientific papers. For table understanding, a metric-type is essential to
discriminate numbers in the tables. We introduce a new information extraction
task, metric-type identification from multi-level header numerical tables, and
provide a dataset extracted from scientific papers consisting of header tables,
captions, and metric-types. We then propose two joint-learning neural
classification and generation schemes featuring pointer-generator-based and
BERT-based models. Our results show that the joint models can handle both
in-header and out-of-header metric-type identification problems.
