Researchers have developed a new two-stage pipeline for extracting structured data from spreadsheets, addressing challenges posed by diverse formats and layouts. The system first classifies cell types using a LightGBM classifier combined with a conditional random field for spatial consistency, then employs a deterministic algorithm for table detection. This approach, evaluated on the new StatSheets benchmark, achieves high accuracy in both cell-type classification and table detection, outperforming or remaining competitive with existing LLM-based systems while requiring fewer computational resources. AI
IMPACT This research offers a more computationally efficient alternative to LLM-based systems for spreadsheet data extraction, potentially improving scalability for data analysis tasks.
RANK_REASON The cluster contains a research paper detailing a new method for spreadsheet table understanding.
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- alphaXiv
- arXiv
- CatalyzeX
- conditional random field
- DagsHub
- Gotit.pub
- Hugging Face
- LightGBM
- ScienceCast
- SpreadsheetLLM
- StatSheets
- TUTA Transformer
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