Extracting key insights from earnings call transcript via information-theoretic contrastive learning

Earnings conference calls provide critical insights into a company’s financial health, future outlook, and strategic direction. Traditionally, analysts manually analyze these lengthy transcripts to extract key information, a process that is both time-consuming and prone to bias and error. To address this, text mining tools, particularly extractive summarization, are increasingly being used to automatically extract key insights, aiming to standardize the analysis process and improve efficiency. Extractive summarization automates the selection of the most informative sentences, offering a promising solution for transcript analysis. However, existing extractive summarization techniques face several challenges, such as the lack of labeled training data, difficulties in incorporating domain-specific knowledge, and inefficiencies in handling large-scale datasets. In this work, we introduce ECT-SKIE, an information-theoretic, self-supervised approach for extracting key insights from earnings call transcripts. We leverage variational information bottleneck theory to extract insights in parallel, significantly accelerating the process. In addition, we propose a structure-aware contrastive learning strategy that enables model training without the need for labeled data. We further develop a novel container-based key sentence extractor to alleviate sentence redundancy. Using a large-scale dataset of U.S. market earnings call transcripts, we evaluate our method against nine representative baselines across three downstream tasks. Experimental results show that ECT-SKIE can consistently extract high-quality key sentences. The code is publicly available at: https://github.com/MongoTap/ECT-SKIE.