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Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers

2021-02-01 15:09:36
Lya Hulliyyatus Suadaa, Hidetaka Kamigaito, Manabu Okumura, Hiroya Takamura
arXiv_CL
           
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2102.00819

PDF

https://arxiv.org/pdf/2102.00819.pdf

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