Choosing the Right Approach: LLMs vs. Traditional Machine Learning for Text Summarization

In an era where natural language processing (NLP) tools are becoming increasingly sophisticated and accessible, many look to automate text-related processes such as recognition, summarization, and generation to save crucial time and effort. Currently, both machine learning (ML) models and large language models (LLMs) are being used extensively for NLP. Choosing a model to use is dependent on various factors depending on client needs and consultant team capabilities. Summarization through machine learning has come a long way throughout the years, and is now an extremely viable and attractive option for those looking to automate natural language processing.  In this blog, I will dive into the history of NLP and compare and contrast LLMs, machine learning models, and summarization methods.