Application of the TextRank Algorithm for Indonesian News Text Summarization Using Word2Vec and LSA - Nandana, Prana Wijaya Pratama; Faisal, Muhammad (Proceedings of the International Conference on Green Technology; Vol 15 No 1 (2025): Proceeding of 15th International Conference on Green Technology, 2026)
In the digital era, the increasing volume of online news makes it difficult for readers to identify relevant and trustworthy information. This study proposes an extractive summarization system for Indonesian news articles by combining the TextRank algorithm with Word2Vec and Latent Semantic Analysis (LSA). Unlike previous methods such as LexRank, YAKE, and Genetic Algorithm (GA), which have shown limited F1-scores around 0.453, this study introduces a novel integration of semantic and graph-based techniques to improve summary relevance and coherence. Experiments were conducted using the IndoSum dataset comprising 5,000 news articles. The system was evaluated using ROUGE metrics and manual validation. The best performance was achieved at a 30% compression level, yielding ROUGE-1 of 0.4808, ROUGE-2 of 0.3433, and ROUGE-L of 0.4675. Manual evaluation also confirmed that the generated summaries were more informative and readable compared to those from other compression levels. These results indicate that the proposed approach contributes to the advancement of automatic summarization techniques in the field of natural language processing, particularly for the Indonesian language.