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

arXiv: https://arxiv.org/abs/2102.00819 · HTML: https://mlsanity.com/paper/2102.00819/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction](https://mlsanity.com/paper/1906.09317/) (2019)
- [The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain](https://mlsanity.com/paper/2006.03039/) (2020)
- [Neural Machine Translation with Supervised Attention](https://mlsanity.com/paper/1609.04186/) (2016)
- [A framework for information extraction from tables in biomedical literature](https://mlsanity.com/paper/1902.10031/) (2019)

## 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.