@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Effective Performance Measurement: Challenges and Opportunities in KPI Extraction from Earnings Calls
%A Aavang, Rasmus T.
%A Tjalk-Bøggild, Rasmus
%A Iolov, Alexandre
%A Rizzi, Giovanni
%A Zhang, Mike
%A Bjerva, Johannes
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F aavang-etal-2026-effective
%X 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.
%U https://aclanthology.org/2026.acl-industry.100/
%P 1434-1460
Markdown (Informal)
[Effective Performance Measurement: Challenges and Opportunities in KPI Extraction from Earnings Calls](https://aclanthology.org/2026.acl-industry.100/) (Aavang et al., ACL 2026)
ACL