Copying tables from documents and applications without proper tabular support, like PDF documents, web pages or images, surprisingly remains a challenge. In this paper, we present Revilio, a novel neurosymbolic system for reconstructing tables when their column boundaries have been lost. Revilio addresses this task by detecting headers, generating an initial table sketch using a large language model, and using that sketch as a guiding representation during an enumerate-and-test strategy that evaluates syntactic and semantic table structures. We evaluate Revilio on a diverse set of datasets, demonstrating significant improvements over existing table parsing methods. Revilio outperforms traditional techniques in both accuracy and scalability, handling large tables with over 100,000 rows. Our experiments find an increase in reconstruction accuracy by 5.8\u201311.3% over both neural and symbolic baseline systems.<\/p>\n","protected":false},"excerpt":{"rendered":"
Copying tables from documents and applications without proper tabular support, like PDF documents, web pages or images, surprisingly remains a challenge. In this paper, we present Revilio, a novel neurosymbolic system for reconstructing tables when their column boundaries have been lost. Revilio addresses this task by detecting headers, generating an initial table sketch using a 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