Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers
This work addresses a specific information extraction challenge for scientific document analysis, presenting an incremental advancement in table understanding.
The paper tackles the problem of metric-type identification in multi-level header numerical tables from scientific papers by introducing a new dataset and proposing joint-learning neural models, achieving the ability to handle both in-header and out-of-header identification tasks.
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.