Today, people can easily connect to the Internet anytime, anywhere. They can check the weather, find nearby restaurants, locate places on maps, and easily find various venues for reading the news. Due to the spread of the Internet and the development of speed, the number of news outlets has increased exponentially. As a result, people can access news articles through various methods. People primarily use smartphones, computers, or tablets to access the news. There are many ways people read the news. Some people may want to read the entire content of an article, while others may want to read only the title and a simple summary. For this reason, many studies have been conducted on news summaries. Some media outlets provide their own news texts and summaries, but there are many places that do not. Summarizing the news is time-consuming, and shorter news articles are also difficult to summarize. Current news summary methods require labeled data created by humans. To address these limitations, we propose a hybrid summarization system that combines extractive and abstractive methods. The proposed method extracts the news text to create label data and uses this data to perform abstractive summarization. The purpose is to compare the summary obtained through this process with the results of the existing general summary and to make as similar results as possible without label data.