KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents
This addresses the need for better relation extraction tools in finance, but it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of extracting Key Performance Indicators (KPIs) from financial documents by introducing KPI-EDGAR, a novel dataset for joint Named Entity Recognition and Relation Extraction, and proposes a new metric with a word-level weighting scheme to improve evaluation accuracy.
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.