Beyond String Matching: Semantic Evaluation of PDF Table Extraction
Reliably extracting tables from PDFs is essential for large-scale scientific data mining and knowledge base construction, yet existing evaluation approaches rely on rule-based metrics that fail to capture semantic equivalence of table content. We present a benchmarking framework based on synthetically generated PDFs with precise LaTeX ground truth, using tables sourced from arXiv to ensure realistic complexity and diversity. As our central methodological contribution, we apply LLM-as-a-judge for semantic table evaluation, integrated into a matching pipeline that accommodates inconsistencies in parser outputs. Through a human validation study comprising over 1,500 quality judgments on extr...
| Authors | Pius Horn, Janis Keuper |
|---|---|
| Primary category | cs.CV |
| Published | 19 March 2026 |
| Last updated at source | 1 June 2026 |
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- ankitt6174/pdf-parse-benchDataset · cited by
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- piushorn/pdf-parse-benchDataset
- ankitt6174/pdf-parse-benchDataset
- piushorn/arxiv-latex-tables-43kDataset
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