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SciDaSynth: Interactive Structured Data Extraction From Scientific Literature With Large Language Model.

Xingbo Wang1,2, Samantha L Huey3, Rui Sheng4

  • 1Present Address: Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI) Sunnyvale California USA.

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Summary
This summary is machine-generated.

SciDaSynth, a new system using large language models, efficiently extracts and structures data from scientific documents. It aids researchers in creating high-quality data tables from diverse sources, improving scientific knowledge discovery.

Keywords:
data extractionknowledge baselarge language modelsscientific literature

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Area of Science:

  • Scientific data extraction
  • Information retrieval
  • Knowledge discovery

Background:

  • The rapid growth of scientific literature necessitates efficient data extraction.
  • Existing tools struggle with multimodal and inconsistent data formats.
  • Structured data is crucial for evidence-based decision-making.

Purpose of the Study:

  • To introduce SciDaSynth, an interactive system for automated structured data table generation.
  • To enable data integration from diverse sources like text, tables, and figures.
  • To support efficient data validation and refinement for cross-document consistency.

Main Methods:

  • Utilizing large language models (LLMs) for data extraction and structuring.
  • Developing an interactive system for user-guided data table generation.
  • Implementing visual summaries and semantic grouping for data validation.

Main Results:

  • SciDaSynth effectively generates structured data tables from multimodal sources.
  • The system demonstrated higher efficiency in producing high-quality structured data compared to baseline methods.
  • Researchers confirmed the system's utility in resolving cross-document data inconsistencies.

Conclusions:

  • SciDaSynth offers a novel approach to structured data extraction from scientific literature.
  • The system enhances the efficiency and quality of data synthesis for researchers.
  • Design implications for human-AI collaborative data extraction systems are discussed.