Comparison of News Text Summarization Using NLTK and TextRank Based on Python Programming - Surniandari, Artika; Rachmi, Hilda; Fauzi, Ahmad; Utami, Lila Dini (Universitas Bina Sarana Informatika, 2025)
Text summarization technology is increasingly used to simplify the vast amount of news information available in the digital era. This study compares two popular text summarization methods, the Natural Language Toolkit (NLTK) and TextRank, in the context of news summarization using the Python programming language. The goal of this research is to evaluate the performance of both algorithms based on summary quality and processing time. The dataset comprises a collection of news articles in Indonesian, processed using both methods. The results indicate that each algorithm has distinct advantages: TextRank excels in generating more coherent summaries, while NLTK demonstrates faster processing times. This study aims to contribute insights into the selection of an appropriate text summarization method for automating news summarization across various applications.