Efficiently extracting data from tables in the scientific literature is
pivotal for building large-scale databases. However, the tables reported in
materials science papers exist in highly diverse forms; thus, rule-based
extractions are an ineffective approach. To overcome this challenge, we present
MaTableGPT, which is a GPT-based table data extractor from the materials
science literature. MaTableGPT features key strategies of table data
representation and table splitting for better GPT comprehension and filtering
hallucinated information through follow-up questions. When applied to a vast
volume of water splitting catalysis literature, MaTableGPT achieved an
extraction accuracy (total F1 score) of up to 96.8%. Through comprehensive
evaluations of the GPT usage cost, labeling cost, and extraction accuracy for
the learning methods of zero-shot, few-shot and fine-tuning, we present a
Pareto-front mapping where the few-shot learning method was found to be the
most balanced solution owing to both its high extraction accuracy (total F1
score>95%) and low cost (GPT usage cost of 5.97 US dollars and labeling cost of
10 I/O paired examples). The statistical analyses conducted on the database
generated by MaTableGPT revealed valuable insights into the distribution of the
overpotential and elemental utilization across the reported catalysts in the
water splitting literature.

利用 MaTableGPT 从材料科学文献中高效提取表格数据，成功实现了 96.8% 的提取准确率，并展示了几乎无错误的学习方法的成本分析和 Pareto 前沿映射，为水分解催化领域的研究提供了宝贵的洞察。

MaTableGPT: 材料科学文献中基于 GPT 的表格数据提取器

MaTableGPT: GPT-based Table Data Extractor from Materials Science  Literature

The FA team participated in the Table Data Extraction (TDE) and Text-to-Table
Relationship Extraction (TTRE) tasks of the NTCIR-17 Understanding of
Non-Financial Objects in Financial Reports (UFO). This paper reports our
approach to solving the problems and discusses the official results. We
successfully utilized various enhancement techniques based on the ELECTRA
language model to extract valuable data from tables. Our efforts resulted in an
impressive TDE accuracy rate of 93.43 %, positioning us in second place on the
Leaderboard rankings. This outstanding achievement is a testament to our
proposed approach's effectiveness. In the TTRE task, we proposed the rule-based
method to extract meaningful relationships between the text and tables task and
confirmed the performance.

本研究报告了我们在 NTCIR-17《金融报告中非金融对象的理解》（UFO）的 Table Data Extraction（TDE）和 Text-to-Table Relationship Extraction（TTRE）任务中解决问题的方法，并讨论了官方结果。我们成功地利用基于 ELECTRA 语言模型的各种增强技术来从表格中提取有价值的数据，使我们在 TDE 任务中取得了 93.43% 的令人瞩目的准确率，在排行榜上排名第二。这一卓越成就证明了我们提出的方法的有效性。在 TTRE 任务中，我们提出了基于规则的方法来提取文本和表格之间的有意义的关系，并验证了其性能。