Enhancing Cable News Network Comprehension: Text Rank Integrated Natural Language Processing Summary Algorithm - Ramadhan, Duta Pramudya; Hindarto, Djarot (Sinkron : jurnal dan penelitian teknik informatika, 2024)
In the online news space, timely content delivery has become essential due to the unavoidable information overload. This study investigates the use of Python-based text summarizing techniques on news sites, promoting the combination of Natural Language Processing approaches with the Text Rank summarization algorithm. The primary objective is to deliver automatic news article summaries while preserving pertinent information, this is confirmed by means of experimental testing. This study uses the Text Rank technique on a news platform to enhance summaries' readability and information absorption capacity. To test the Text Rank algorithm's capacity to provide enlightening summaries, two news stories from the Cable News Network were chosen for the experiment. The word "Trump" obtained the highest score of 16.52 when sentence scores were calculated using the Text Rank algorithm. "Former" came in second with a score of 1.95, "McCarthy" was third with a score of 1.31, and "President" and "Republican" were each awarded a score of 1.03. Furthermore, the terms "CNN" and "Establishment" received scores of 0.79 and 0.58, respectively, for "DeSantis" and "Endorsements." Reader accessibility and convenience can be improved by using a news summary algorithm on a Python-based platform to swiftly retrieve important information. This research emphasizes the critical role that summary algorithm technology plays in enabling efficient and easily accessible information consumption in the digital age, in addition to creating automated tools for news summaries.