# KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents

arXiv: https://arxiv.org/abs/2210.09163 · HTML: https://mlsanity.com/paper/2210.09163/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision](https://mlsanity.com/paper/2006.15509/) (2020)
- [Double Graph Based Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2009.13752/) (2020)
- [Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders](https://mlsanity.com/paper/2010.03851/) (2020)
- [Automated Concatenation of Embeddings for Structured Prediction](https://mlsanity.com/paper/2010.05006/) (2020)
- [Document-level Relation Extraction as Semantic Segmentation](https://mlsanity.com/paper/2106.03618/) (2021)
- [Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning](https://mlsanity.com/paper/2105.03654/) (2021)
- [Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction](https://mlsanity.com/paper/2102.10249/) (2021)
- [A Frustratingly Easy Approach for Entity and Relation Extraction](https://mlsanity.com/paper/2010.12812/) (2020)
- [T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition](https://mlsanity.com/paper/2209.12616/) (2022)
- [EDGAR-CORPUS: Billions of Tokens Make The World Go Round](https://mlsanity.com/paper/2109.14394/) (2021)
- [Interpretable Multi-dataset Evaluation for Named Entity Recognition](https://mlsanity.com/paper/2011.06854/) (2020)
- [A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2101.10213/) (2021)
- [KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports](https://mlsanity.com/paper/2208.02140/) (2022)
- [Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers](https://mlsanity.com/paper/2010.04362/) (2020)

## Built on by (most cited first, 9 of 9)

- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models](https://mlsanity.com/paper/2308.06111/) (2023)
- [Self-Specialization: Uncovering Latent Expertise within Large Language Models](https://mlsanity.com/paper/2310.00160/) (2023)
- [Enterprise Benchmarks for Large Language Model Evaluation](https://mlsanity.com/paper/2410.12857/) (2024)
- [HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings](https://mlsanity.com/paper/2502.15411/) (2025)
- [CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases](https://mlsanity.com/paper/2608.27391/) (2026)
- [Controlled Randomness Improves the Performance of Transformer Models](https://mlsanity.com/paper/2310.13526/) (2023)
- [An Automated LLM-based Pipeline for Asset-Level Database Creation to Assess Deforestation Impact](https://mlsanity.com/paper/2505.05494/) (2025)
- [A Pluggable Multi-Task Learning Framework for Sentiment-Aware Financial Relation Extraction](https://mlsanity.com/paper/2506.12452/) (2025)

## Abstract (arXiv)

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.