AI-Driven News Summarisation for Financial Insights: Revolutionising Large Language Models

Abstract

Financial professionals rely on timely news to support decision making. However, manually reviewing large volumes of financial news is inefficient particularly when articles are complex and lengthy. Automated text summarisation using large language models (LLMs) offers a promising solution for summarising extensive textual information. This study aims to customise and optimise a text summarisation model for financial news. To achieve this, the study examines the effectiveness of transformer based LLMs by fine-tuning the FLAN-T5-XL model for this domain. Experiments were conducted using a dataset of 2,000 general news articles. Performance was assessed using ROUGE metrics and expert human evaluation. The results show that the fine-tuned FLAN-T5-XL with truncation achieved the best performance obtaining a ROUGE -1 score of 55 and 86% agreement with expert evaluation. These findings demonstrate that domain adapted LLMs can provide a practical tool for rapid information synthesis and financial decision making.