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New dataset HiFi-KPI aids hierarchical KPI extraction from financial reports

Researchers have introduced HiFi-KPI, a new dataset designed to improve the extraction of Key Performance Indicators (KPIs) from financial earnings reports. The dataset, comprising 1.65 million paragraphs and nearly 200,000 hierarchical labels, aims to enhance the transferability of KPI tagging across different companies. Initial evaluations show that encoder-based models achieve high accuracy in classification, while large language models demonstrate moderate success in structured extraction, with errors often related to date discrepancies. AI

IMPACT Enhances NLP capabilities for financial data analysis, potentially improving investment strategies.

RANK_REASON The cluster contains a research paper detailing a new dataset for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rasmus Aavang, Giovanni Rizzi, Rasmus B{\o}ggild, Alexandre Iolov, Mike Zhang, Johannes Bjerva ·

    HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings

    arXiv:2502.15411v4 Announce Type: replace-cross 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 …