Exploring Summarization Performance: A Comparison of Pointer Generator, Pegasus, and GPT-3 Models

The world is rapidly advancing technologically and the way we communicate is changing with it.We are now able to send messages through text, voice, or video chat, which means that the amount of content being generated every day is growing fast. The problem with all this information is that it can be overwhelming for people to stay on top of everything they need to,especially those who have to read a lot of texts and get the gist of what they need to know. Manual text summarizing is a time-consuming and inherently tedious activity. There is a need for a system that could provide crisp, precise information on a real time basis . This would help the decision makers to comprehend the information quickly and take decisions at a faster rate. This also helps to read important information without having to spend too much time on it. By doing so, you save your time and energy, as well as reduce stress levels. This research paper identifies the need for automating the text summarization . It further elaborates on its key concepts and gives a comparison of the various text summarization models. Moving into a proposed model in itself was a challenge. This research paper dwells on the proposed methodology and performs various evaluation metrics. ImpactStatement - The process of text summarization involves using natural language processing to condense information into a shorter, more concise version. The goal is to condense the original document while preserving its essential information This paper conducts a comparison of different text summarization methods, including extractive and abstractive techniques. It also categorizes summarization systems and examines the use of statistical and linguistic approaches for summarization.