Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers
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Abstract (arXiv)
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.
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References · 10
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- 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
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- 2019Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Constructioncited by 50
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- 2016Neural Machine Translation with Supervised Attentioncited by 31
- 2019A framework for information extraction from tables in biomedical literaturecited by 4
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