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    <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 2.5K-instance 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 Anonymous.</abstract>
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%0 Conference Proceedings
%T HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings
%A Aavang, Rasmus T.
%A Rizzi, Giovanni
%A Tjalk-Bøggild, Rasmus
%A Iolov, Alexandre
%A Zhang, Mike
%A Bjerva, Johannes
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F aavang-etal-2026-hifi
%X 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 2.5K-instance 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 Anonymous.
%R 10.63317/2nbsp7zzfb3g
%U https://aclanthology.org/2026.lrec-1.30/
%U https://doi.org/10.63317/2nbsp7zzfb3g
%P 441-455

Markdown (Informal)

[HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings](https://aclanthology.org/2026.lrec-1.30/) (Aavang et al., LREC 2026)

• HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings (Aavang et al., LREC 2026)

ACL

• Rasmus T. Aavang, Giovanni Rizzi, Rasmus Tjalk-Bøggild, Alexandre Iolov, Mike Zhang, and Johannes Bjerva. 2026. HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 441–455, Palma de Mallorca, Spain. ELRA Language Resource Association.