The article discusses how to group large amounts of text into clusters based on their similarities, a process known as text clustering. By using techniques like hierarchical clustering and K-means clustering, researchers can identify topics within the text and extract key ideas. This helps in organizing and retrieving information from vast amounts of text data available on the web. The study also explores the challenges and issues involved in text clustering and topic identification, providing insights into how to effectively categorize and extract important information from text.