Lessons in Benchmarking: Earnings Call Summarization
Earnings calls are a critical source of information for investors, analysts, and stakeholders, providing insights into a company’s financial performance, strategies, and future outlook. However, the sheer volume and complexity of these calls present challenges in extracting relevant information efficiently. This presentation explores the lessons learned in Aiera’s benchmarking for abstractive summarization of earnings calls, focusing on the effectiveness, accuracy, and practicality of different summarization techniques and evaluation metrics.
Jacqueline Garrahan
Senior Machine Learning Engineer at Aiera
Jacqueline Garrahan has a diverse range of work experience in the fields of machine learning, data science, and engineering. Jacqueline began their career as an Infectious Disease Intern at Cambridge Health Alliance in 2012. After that, they served as a Vice President at TWLOHA-BC from 2013 to 2014. In 2016, they held positions as a Semiconductor Characterization Intern at AddiLat Inc. and a Staff Engineer at the Healthcare Systems Engineering Institute of Northeastern University (HSyE).