FIN2SUM: Advancing AI-Driven Financial Text Summarization with LLMs

In the modern financial sector, the rapid digitalization of financial reports necessitates efficient and reliable text summarization tools. This research introduces FIN2SUM, a novel framework designed for summarizing the managerial analysis and discussion sections of 10-K reports from top NASDAQ-listed companies. The study aims to evaluate Large Language Models (LLMs) in financial text summarization, highlighting LLAMA-2's adeptness in processing complex financial information, thus making FIN2SUM a vital tool for analysts and decision-makers. The methodology includes a thorough evaluation of three state-of-the-art LLMs—LLAMA-2, FLAN, and Claude 2—using BERT and ROUGE scores. The research concludes that FIN2SUM, enhanced by LLAMA-2, significantly advances AI-driven financial text summarization.