← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-industry.100 · 记录 #1
Effective Performance Measurement: Challenges and Opportunities in KPI Extraction from Earnings Calls
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:08
摘要
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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记录 #1 · 2026-07 · 最新观测版本
Effective Performance Measurement: Challenges and Opportunities in KPI Extraction from Earnings Calls
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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