Main Article Content
In this modern age, where vast quantities of data are accessible on the Internet, it is crucial to provide a better mechanism for extracting information quickly and efficiently. Manually extracting the description of a huge text document is incredibly hard and time-consuming. On the Internet, there is a wealth of text content. As a result, finding relevant documents among the large set of documents available and extracting necessary details from them is a challenge. Automatic text summarization is critical for solving the two problems listed above. The method of identifying the most important and pertinent material in a document or a group of related documents and compacting it into a condensed version while maintaining its overall significance is known as text summarization. Before precluding text summarization, it’s important to know the actual import of the Summary. A summary is a text that extracts information from one or more texts and conveys it concisely. The aim of Automatic Text summarization is to covert the source material into a semantically shorter adaption. The most relevant benefit of using a summary is that it shrinks the amount of time it takes to comprehend. Extractive and Abstractive are two types of content summarization techniques. An extractive summary technique involves selecting key sentences, pieces, and other elements from the original report and connecting them into a more manageable structure. An abstractive method is an apprehension of the key ideas in a text and then expressions of those ideas in a plain regular language.