Multidocument Abstractive Summarization using Abstract Meaning Representation for Indonesian Language

Abstractive summarization based on Abstract Meaning Representation (AMR) graphs employs predicate-argument structures to generate more coherent summarization. However, this approach is designed only for single document summarization of English news article. In this paper, we develop AMR graph-based multi-document summaries for Indonesian news articles. The representation of Indonesian AMR graph is aided by a set of rules and dictionaries. AMR graph is selected as a summary graph by performing feature extraction, applying Integer Linear Programming (ILP), and determining parameters with perceptron. Multidocument summarization is implemented by selecting sentences which will be summarized with Agglomerative Hierarchical Clustering. Experiments were carried out to determine the best number of epochs carried out for weight learning used for graph selection, and determine the threshold of the distance between cluster members produced by Agglomerative Hierarchical Clustering. The results of the tests performed were ROUGE-1 and ROUGE-2. The best result for ROUGE-1 and ROUGE-2 are 0.78947 and 0.55556.