Comparison of News Text Summarization Using NLTK and TextRank Based on Python Programming

Artika Surniandari, Hilda Rachmi, Ahmad Fauzi, Lila Dini Utami

Abstract


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.

Keywords


text summarization NLTK TextRank, Python, news, text summarization

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DOI: https://doi.org/10.31294/jtk.v11i1.24497

Copyright (c) 2025 Artika Surniandari, Hilda Rachmi, Ahmad Fauzi, Lila Dini Utami

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ISSN: 2442-2436 (print), and 2550-0120


 dipublikasikan oleh LPPM Universitas Bina Sarana Informatika Jakarta

Jl. Kramat Raya No.98, Kwitang, Kec. Senen, Kota Jakarta Pusat, DKI Jakarta 10450
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License