@inproceedings{aavang-etal-2026-effective,
    title = "Effective Performance Measurement: Challenges and Opportunities in {KPI} Extraction from Earnings Calls",
    author = "Aavang, Rasmus T.  and
      Tjalk-B{\o}ggild, Rasmus  and
      Iolov, Alexandre  and
      Rizzi, Giovanni  and
      Zhang, Mike  and
      Bjerva, Johannes",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.100/",
    pages = "1434--1460",
    ISBN = "979-8-89176-394-4",
    abstract = "Earnings calls are a key source of financial information about public companies. However, extracting information from these calls is difficult.Unlike the templatic filings required by the U.S. Securities and Exchange Commission (SEC) to report a company{'}s financial situation, earnings conference calls have no built-in labels, are unstructured, and feature conversational language.We explore this challenging domain by assessing the information captured by models trained on SEC filings and in-context learning methods. To establish a baseline, we first evaluate the generalization capabilities of SEC-trained models across established SEC datasets.To support our investigation, we introduce three novel benchmarks: (1) SEC Filings Benchmark (SECB), (2) Earnings Calls Benchmark (ECB), and ECB-A, a subset with 5,346 expert annotations to support our qualitative analysis.We find that encoder-based models struggle with the domain shift. Finally, we propose a system utilizing LLMs to perform open-ended extraction from unstructured call transcripts, verified by human evaluation (79.7{\%} precision), providing a baseline for this valuable domain through the consistent tracking of emergent KPIs."
}