# DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles

arXiv: https://arxiv.org/abs/2207.01079 · HTML: https://mlsanity.com/paper/2207.01079/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 73 references that are in the map.
- First posted on arXiv 3 Jul 2022.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://mlsanity.com/paper/2107.07653/) (2021)
- [TABBIE: Pretrained Representations of Tabular Data](https://mlsanity.com/paper/2105.02584/) (2021)
- [INFOTABS: Inference on Tables as Semi-structured Data](https://mlsanity.com/paper/2005.06117/) (2020)
- [MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction](https://mlsanity.com/paper/2109.15290/) (2021)
- [The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures](https://mlsanity.com/paper/1905.06939/) (2019)
- [Capturing Row and Column Semantics in Transformer Based Question Answering over Tables](https://mlsanity.com/paper/2104.08303/) (2021)
- [Answering Conversational Questions on Structured Data without Logical Forms](https://mlsanity.com/paper/1908.11787/) (2019)
- [Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study](https://mlsanity.com/paper/2106.09700/) (2021)
- [Extracting a Knowledge Base of Mechanisms from COVID-19 Papers](https://mlsanity.com/paper/2010.03824/) (2020)
- [CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering](https://mlsanity.com/paper/2106.04441/) (2021)
- [Topic Transferable Table Question Answering](https://mlsanity.com/paper/2109.07377/) (2021)

## Built on by (most cited first, 17 of 17)

- [SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature](https://mlsanity.com/paper/2406.07835/) (2024)
- [Are LLMs Ready for Real-World Materials Discovery?](https://mlsanity.com/paper/2402.05200/) (2024)
- [Are large language models superhuman chemists?](https://mlsanity.com/paper/2404.01475/) (2024)
- [Probing the limitations of multimodal language models for chemistry and materials research](https://mlsanity.com/paper/2411.16955/) (2024)
- [Schema-Driven Information Extraction from Heterogeneous Tables](https://mlsanity.com/paper/2305.14336/) (2023)
- [HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science](https://mlsanity.com/paper/2310.08511/) (2023)
- [Foundational Large Language Models for Materials Research](https://mlsanity.com/paper/2412.09560/) (2024)
- [MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models](https://mlsanity.com/paper/2308.09115/) (2023)
- [ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models](https://mlsanity.com/paper/2410.22360/) (2024)
- [Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction](https://mlsanity.com/paper/2310.08383/) (2023)
- [MatViX: Multimodal Information Extraction from Visually Rich Articles](https://mlsanity.com/paper/2410.20494/) (2024)
- [MDAgent2: Large Language Model for Code Generation and Knowledge Q&A in Molecular Dynamics](https://mlsanity.com/paper/2601.02075/) (2026)
- [Intent-Aware Schema Generation And Refinement For Literature Review Tables](https://mlsanity.com/paper/2507.19521/) (2025)
- [AutoPK: Leveraging LLMs and a Hybrid Similarity Metric for Advanced Retrieval of Pharmacokinetic Data from Complex Tables and Documents](https://mlsanity.com/paper/2510.00039/) (2025)
- [Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research](https://mlsanity.com/paper/2502.12669/) (2025)
- [From Papers to Property Tables: A Priority-Based LLM Workflow for Materials Data Extraction](https://mlsanity.com/paper/2604.07584/) (2026)
- [ArticleMiner: Ontology-Guided Knowledge Graph Construction from Scientific Publications](https://mlsanity.com/paper/2609.25607/) (2026)

## Abstract (arXiv)

A crucial component in the curation of KB for a scientific domain (e.g., materials science, foods & 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 a strong baseline DISCOMAT, 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.