  We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition
and Relation Extraction building on financial reports uploaded to the
Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, where the
main objective is to extract Key Performance Indicators (KPIs) from financial
documents and link them to their numerical values and other attributes. We
further provide four accompanying baselines for benchmarking potential future
research. Additionally, we propose a new way of measuring the success of said
extraction process by incorporating a word-level weighting scheme into the
conventional F1 score to better model the inherently fuzzy borders of the
entity pairs of a relation in this domain.
