HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings
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Computer Science > Computation and Language
Title:HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings
Abstract:Accurate tagging of earnings reports can yield significant short-term returns for stakeholders. The machine-readable inline eXtensible Business Reporting Language (iXBRL) is mandated for public financial filings. Yet, its complex, fine-grained taxonomy limits the cross-company transferability of tagged Key Performance Indicators (KPIs). To address this, we introduce the Hierarchical Financial Key Performance Indicator (HiFi-KPI) dataset, a large-scale corpus of 1.65M paragraphs and 198k unique, hierarchically organized labels linked to iXBRL taxonomies. HiFi-KPI supports multiple tasks and we evaluate three: KPI classification, KPI extraction, and structured KPI extraction. For rapid evaluation, we also release HiFi-KPI-Lite, a manually curated 8K paragraph subset. Baselines on HiFi-KPI-Lite show that encoder-based models achieve over 0.906 macro-F1 on classification, while Large Language Models (LLMs) reach 0.440 F1 on structured extraction. Finally, a qualitative analysis reveals that extraction errors primarily relate to dates. We open-source all code and data at this https URL.
| Comments: | Camera-ready. Accepted at LREC 2026 (main conference) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2502.15411 [cs.CL] |
| (or arXiv:2502.15411v5 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2502.15411
arXiv-issued DOI via DataCite
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| Journal reference: | Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026), pages 441-455, Palma, Mallorca, Spain. ELRA, 2026 |
| Related DOI: | https://doi.org/10.63317/2nbsp7zzfb3g
DOI(s) linking to related resources
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Submission history
From: Rasmus T. Aavang [view email][v1] Fri, 21 Feb 2025 12:19:08 UTC (1,000 KB)
[v2] Mon, 24 Feb 2025 14:45:27 UTC (1,003 KB)
[v3] Thu, 19 Mar 2026 10:06:48 UTC (2,188 KB)
[v4] Mon, 1 Jun 2026 13:37:16 UTC (2,188 KB)
[v5] Thu, 20 Aug 2026 12:06:58 UTC (2,188 KB)
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