@inproceedings{gupta-etal-2023-discomat,
    title = "{D}i{SC}o{M}a{T}: Distantly Supervised Composition Extraction from Tables in Materials Science Articles",
    author = "Gupta, Tanishq  and
      Zaki, Mohd  and
      Khatsuriya, Devanshi  and
      Hira, Kausik  and
      Krishnan, N M Anoop  and
      {Mausam}",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.753",
    doi = "10.18653/v1/2023.acl-long.753",
    pages = "13465--13483",
    abstract = "A crucial component in the curation of KB for a scientific domain (e.g., materials science, food {\&} nutrition, fuels) is information extraction from tables in the domain{'}s published research articles. To facilitate research in this direction, we define a novel NLP task of extracting compositions of materials (e.g., glasses) from tables in materials science papers. The task involves solving several challenges in concert, such as tables that mention compositions have highly varying structures; text in captions and full paper needs to be incorporated along with data in tables; and regular languages for numbers, chemical compounds, and composition expressions must be integrated into the model. We release a training dataset comprising 4,408 distantly supervised tables, along with 1,475 manually annotated dev and test tables. We also present DiSCoMaT, a strong baseline that combines multiple graph neural networks with several task-specific regular expressions, features, and constraints. We show that DiSCoMaT outperforms recent table processing architectures by significant margins. We release our code and data for further research on this challenging IE task from scientific tables.",
}

A crucial component in the curation of KB for a scientific domain (e.g., materials science, food {\&} nutrition, fuels) is information extraction from tables in the domain{'}s published research articles. To facilitate research in this direction, we define a novel NLP task of extracting compositions of materials (e.g., glasses) from tables in materials science papers. The task involves solving several challenges in concert, such as tables that mention compositions have highly varying structures; text in captions and full paper needs to be incorporated along with data in tables; and regular languages for numbers, chemical compounds, and composition expressions must be integrated into the model. We release a training dataset comprising 4,408 distantly supervised tables, along with 1,475 manually annotated dev and test tables. We also present DiSCoMaT, a strong baseline that combines multiple graph neural networks with several task-specific regular expressions, features, and constraints. We show that DiSCoMaT outperforms recent table processing architectures by significant margins. We release our code and data for further research on this challenging IE task from scientific tables.
Gupta, Tanishq Zaki, Mohd Khatsuriya, Devanshi Hira, Kausik Krishnan, N M Anoop {Mausam}
DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles