# Financial Numeric Extreme Labelling: A Dataset and Benchmarking for XBRL Tagging

arXiv: https://arxiv.org/abs/2306.03723 · HTML: https://mlsanity.com/paper/2306.03723/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification](https://mlsanity.com/paper/1811.01727/) (2018)
- [ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts](https://mlsanity.com/paper/2210.12467/) (2022)
- [FiNER: Financial Numeric Entity Recognition for XBRL Tagging](https://mlsanity.com/paper/2203.06482/) (2022)
- [EDGAR-CORPUS: Billions of Tokens Make The World Go Round](https://mlsanity.com/paper/2109.14394/) (2021)
- [Financial Event Extraction Using Wikipedia-Based Weak Supervision](https://mlsanity.com/paper/1911.10783/) (2019)
- [Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction](https://mlsanity.com/paper/1910.05032/) (2019)
- [A Generative Approach for Financial Causality Extraction](https://mlsanity.com/paper/2204.05674/) (2022)

## Built on by (most cited first, 12 of 12)

- [FinBen: A Holistic Financial Benchmark for Large Language Models](https://mlsanity.com/paper/2402.12659/) (2024)
- [A Survey of Large Language Models in Finance (FinLLMs)](https://mlsanity.com/paper/2402.02315/) (2024)
- [FinEntity: Entity-level Sentiment Classification for Financial Texts](https://mlsanity.com/paper/2310.12406/) (2023)
- [FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs](https://mlsanity.com/paper/2510.08886/) (2025)
- [FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets](https://mlsanity.com/paper/2505.19819/) (2025)
- [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025)
- [Finance Language Model Evaluation (FLaME)](https://mlsanity.com/paper/2506.15846/) (2025)
- [HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings](https://mlsanity.com/paper/2502.15411/) (2025)
- [FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data](https://mlsanity.com/paper/2502.18471/) (2025)
- [RKEFino1: A Regulation Knowledge-Enhanced Large Language Model](https://mlsanity.com/paper/2506.05700/) (2025)
- [Noise-Robust Financial Numerical Entity Attribute Tagging](https://mlsanity.com/paper/2605.24910/) (2026)
- [Recognition Is Not Grounding: Benchmarking Multilingual Financial Evidence Extraction](https://mlsanity.com/paper/2610.19432/) (2026)

## Abstract (arXiv)

The U.S. Securities and Exchange Commission (SEC) mandates all public companies to file periodic financial statements that should contain numerals annotated with a particular label from a taxonomy. In this paper, we formulate the task of automating the assignment of a label to a particular numeral span in a sentence from an extremely large label set. Towards this task, we release a dataset, Financial Numeric Extreme Labelling (FNXL), annotated with 2,794 labels. We benchmark the performance of the FNXL dataset by formulating the task as (a) a sequence labelling problem and (b) a pipeline with span extraction followed by Extreme Classification. Although the two approaches perform comparably, the pipeline solution provides a slight edge for the least frequent labels.