Optimalisasi Peringkasan Artikel Teks Bahasa Indonesia dengan Kombinasi TextRank dan Graph Neural Network Sederhana - Syatria, Muhammad Rifqi; Mulyana, Dadang Iskandar (Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 2026)
The delivery of digital information in Indonesian-language news presents challenges in efficiently capturing the core information. This study proposes a combination of the TextRank algorithm and a simple Graph Neural Network (GNN) to improve the quality of automatic text summarization. TextRank is used to construct a sentence graph based on TF-IDF similarity and cosine similarity, followed by training a SimpleGNN model to optimize sentence scores. Evaluations were conducted on 1,000 articles from the Liputan6 dataset using the ROUGE metric (ROUGE-1, ROUGE-2, and ROUGE-L). The results show that this combined method improves performance compared to pure TextRank, especially in capturing semantic relationships between sentences. This study demonstrates that the integration of a simple GNN can enrich representations in graphs and provide more informative and contextual summaries.
Keywords: Automatic Text Summarization; TextRank; Graph Neural Network; SimpleGNN; ROUGE; Indonesian; Liputan6; Natural Language Processing