Automatic Single Document Summarization For Indonesian News Article Using Abstract Meaning Representation

With the increasing number of online news sources, effective summarization becomes essential to provide readers with concise and informative content. This study focuses on developing an automatic summarization system for single Indonesian news articles using Abstract Meaning Representation (AMR). Leveraging a machine learning-based AMR parser, the system constructs sentence representations, selects subgraphs to build summary graphs, and generates summary texts. The baseline uses retrained Word2Vec and selects the top three most similar sentences via cosine similarity for ROUGE evaluation against IndoSum's abstractive summary. Despite not surpassing baseline performance, the proposed system achieves an average ROUGE-l of 0.62833, ROUGE-2 of 0.54449, and ROUGE-L of 0.58889. The findings indicate that while the proposed system effectively summarizes, it tends to prioritize initial sentences during subgraph selection, which is crucial for constructing accurate summary graphs. This tendency highlights areas for further improvement. Future research can build upon these findings by employing advanced graph construction algorithms for summary graphs and alternative text generation techniques. This study contributes to ongoing efforts to enhance text summarization systems and provides valuable lessons for future research in this field.