Unveiling Patterns in Online English Business News in Sri Lanka: Abstractive Text Summarization with Large Language Models

Abstract

ABSTRACT The emergence of online business news has amplified the rapid generation of large volumes of textual data, making it an important resource for businesses to extract business insights to support business decision making. The aim of this research project is to propose an approach to leverage online English business news to gain business insights with specific focus on Sri Lanka. Accordingly, the research employs a combined approach of document classification, document clustering, and abstractive text summarization methods to utilize large amount online business news data to gain meaningful insights. The research methodology incorporates Bidirectional Encoder Representations from Transformers (BERT) model for news classification, followed by K-means news clustering, and cluster-wise abstractive text summarization utilizing large language models (LLMs), including Llama3, Mistral, and DeepSeek R1 along with retrieval-augmented generation (RAG) to enhance the accuracy and relevance of the generated content. The dataset is obtained from Sri Lankan business news sources, including DailyFT and Lanka Business Online. The ability to fine-tune BERT for specific classification tasks, combined with the scalability of Kmeans clustering and the text generation capabilities of LLMs accommodates unveil business insights from large volumes of textual data. However, there is still room for improvement by experimenting with large datasets and improving above techniques to enhance the performance of proposed approach. The results of the study indicate that the combination of machine learning methods, such as classification and clustering, along with LLMs, yields better results by complementing each method’s strengths, to leverage textual data more efficiently than applying them in isolation. This makes the proposed approach is more pragmatic way of leveraging computational method to handle large amount of online business news. Therefore, current study lays the foundation for harnessing the capabilities of machine learning and LLMs for real-world business applications, specifically in developing tools that support decision-making in business by utilizing online business news data