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.
@article{arxiv.2102.00819,
title = {Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers},
author = {Lya Hulliyyatus Suadaa and Hidetaka Kamigaito and Manabu Okumura and Hiroya Takamura},
journal= {arXiv preprint arXiv:2102.00819},
year = {2021}
}