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        <title>Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers</title>
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    <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>
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        <date>2021-04</date>
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%0 Conference Proceedings
%T Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers
%A Suadaa, Lya Hulliyyatus
%A Kamigaito, Hidetaka
%A Okumura, Manabu
%A Takamura, Hiroya
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F suadaa-etal-2021-metric
%X 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.
%R 10.18653/v1/2021.eacl-main.267
%U https://aclanthology.org/2021.eacl-main.267
%U https://doi.org/10.18653/v1/2021.eacl-main.267
%P 3062-3071

Markdown (Informal)

[Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers](https://aclanthology.org/2021.eacl-main.267) (Suadaa et al., EACL 2021)

• Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers (Suadaa et al., EACL 2021)

ACL

• Lya Hulliyyatus Suadaa, Hidetaka Kamigaito, Manabu Okumura, and Hiroya Takamura. 2021. Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3062–3071, Online. Association for Computational Linguistics.