TableKIE: A Benchmark and Learning Framework TeleTable for Key Information Extraction from Tables

Key information extraction (KIE) from tables goes beyond recognizing textual content and associating values with entities. It requires synthesizing hierarchical row and column headers with contextual information into composite semantic keys that precisely characterize each value. Complex layouts and repeated field labels further challenge vision-language models in linking these keys to the correct values. We introduce TableKIE, a benchmark and learning framework for evaluating and improving table-centric KIE. The benchmark covers two complementary settings: closed-schema extraction, where target fields are predefined, and open-schema extraction, where models identify and organize key information explicitly present in a table without a fixed field inventory. Our primary evaluation focuses on field-value correctness and requested-field coverage. On the subset with verified structural annotations, we additionally evaluate row- and column-path agreement and value-box localization to assess the grounding behavior induced by auxiliary structural supervision.