mlsanityData to 7 Oct 2026

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

arXiv 2207.01079 ↗First posted 3 Jul 2022cs.CL cond-mat.mtrl-sci cs.IRcited by 17

Not on the map. Why?

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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 21 of its 73 references that are in the map. First posted on arXiv 3 Jul 2022.

Builds on

21 of 73 references.

References · 21

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2017Graph Attention Networkscited by 8,854
  5. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  6. 2019TabNet: Attentive Interpretable Tabular Learningcited by 640
  7. 2015Cyclical Learning Rates for Training Neural Networkscited by 627
  8. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
  9. 2020TaBERT: Pretraining for Joint Understanding of Textual and Tabular Datacited by 395
  10. 2018AllenNLP: A Deep Semantic Natural Language Processing Platformcited by 347
  11. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 233
  12. 2021TABBIE: Pretrained Representations of Tabular Datacited by 111
9 more
  1. 2020INFOTABS: Inference on Tables as Semi-structured Datacited by 84
  2. 2021MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extractioncited by 82
  3. 2019The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structurescited by 48
  4. 2021Capturing Row and Column Semantics in Transformer Based Question Answering over Tablescited by 31
  5. 2019Answering Conversational Questions on Structured Data without Logical Formscited by 24
  6. 2021Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Studycited by 23
  7. 2020Extracting a Knowledge Base of Mechanisms from COVID-19 Paperscited by 13
  8. 2021CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answeringcited by 9
  9. 2021Topic Transferable Table Question Answeringcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 7; 2025: 3; 2026: 3.

17 papers.

Sort:

By area:

  1. 2024Are LLMs Ready for Real-World Materials Discovery?cited by 40
  2. 2024SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literaturecited by 37
  3. 2024Are large language models superhuman chemists?cited by 34
  4. 2024Probing the limitations of multimodal language models for chemistry and materials researchcited by 31
  5. 2023Schema-Driven Information Extraction from Heterogeneous Tablescited by 24
  6. 2023HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Sciencecited by 22
  7. 2024Foundational Large Language Models for Materials Researchcited by 18
  8. 2023MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Modelscited by 11
  9. 2023Reconstructing Materials Tetrahedron: Challenges in Materials Information Extractioncited by 8
  10. 2024MatViX: Multimodal Information Extraction from Visually Rich Articlescited by 8
  11. 2024ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Modelscited by 8
  12. 2026MDAgent2: Large Language Model for Code Generation and Knowledge Q&A in Molecular Dynamicscited by 6
5 more
  1. 2025Intent-Aware Schema Generation And Refinement For Literature Review Tablescited by 5
  2. 2025AutoPK: Leveraging LLMs and a Hybrid Similarity Metric for Advanced Retrieval of Pharmacokinetic Data from Complex Tables and Documentscited by 4
  3. 2025Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Researchcited by 2
  4. 2026From Papers to Property Tables: A Priority-Based LLM Workflow for Materials Data Extractioncited by 1
  5. 2026ArticleMiner: Ontology-Guided Knowledge Graph Construction from Scientific Publicationscited by 0