Natural Language Processing-based Summary Algorithm for Understanding Online News

Abstract

System development that can supply the right content at the right moment is necessary due to the internet news industry's ever-growing volume of information. In this context, this study investigates how Python programming language-based text summarizing methods are used on news platforms. The combination of summarization algorithms with Natural Language Processing techniques is recommended by this study. The primary objective is to automatically shorten news items while keeping the essential details. Several experiments are conducted to test the proposed summary algorithm on various news item kinds. This algorithm places emphasis on maintaining critical information while minimizing duplication and guaranteeing consistency and fluidity in summaries. The outcomes of the experiment demonstrate that the summary algorithm in place is capable of efficiently extracting significant information from the news and creating lucid, understandable summaries. The summary provided a high degree of authenticity to the news material, accurately and succinctly summarizing significant facts from the original piece, according to analysis. The accessibility and convenience of reading news can be increased by using summary algorithms for news, a Python-based news platform. It provides readers with a time-saving solution that enables them to swiftly obtain crucial information. In addition to furthering the development of automated tools for news summaries, this study emphasizes the significance of summary algorithm technology in enabling effective and accessible information consumption in the digital age.