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 https://github.com/aaunlp/HiFi-KPI.

翻译：对财报的精准标注能够为利益相关者带来显著的短期收益。虽然公开财务文件强制要求采用机器可读的内联可扩展商业报告语言（iXBRL）格式，但其复杂且细粒度的分类体系限制了跨公司标记关键绩效指标（KPI）的可转移性。为解决此问题，我们提出分层财务关键绩效指标数据集（HiFi-KPI），该大规模语料库包含165万段落与19.8万个与iXBRL分类体系关联的独特分层标签。HiFi-KPI支持多项任务，我们评估了其中三种：KPI分类、KPI提取以及结构化KPI提取。为便于快速评估，我们还发布了经人工精选的8K段落子集HiFi-KPI-Lite。在HiFi-KPI-Lite上的基线实验显示：基于编码器的模型在分类任务上宏F1值超过0.906，而大语言模型（LLMs）在结构化提取任务上F1值达到0.440。最后，定性分析表明提取错误主要与日期相关。我们已在https://github.com/aaunlp/HiFi-KPI 开源所有代码与数据。