Implementasi Algoritma Textrank Untuk Peringkasan Teks Berbahasa Indonesia Pada Aplikasi Berbasis Flask - Johanes Kevin Malau; Daniel Syahputra Tarigan; Dinda Roulita Simangunsong; Rafi Talensi Rajagukguk; Anirma Kandida Br. Ginting (GLOBAL SCIENTS PUBLISHER, 2026)
News articles, academic papers, and online materials are just a few examples of the significant increase in digital text data caused by developments in information technology. As a result, users find it difficult to obtain important information quickly. Therefore, an automatic text summarisation system is urgently needed. The purpose of this study is to apply the TextRank method to summarise Indonesian-language texts and create a web application using the Flask framework. The technique used is the extractive text summarisation method, in which the system selects key sentences from the original text according to their relative importance. The research process includes text pre-processing, TF-IDF representation creation, measurement of the similarity level between phrases, graph creation, and application of the PageRank method to identify the most relevant sentences. The Python programming language with Flask as the backend and a simple web interface were used to build this programme. According to the experimental findings, this system is capable of producing concise text summaries while retaining important data from the original paper. The application can clearly help users understand long text materials more effectively and quickly.