Open AccessSUMMARIZING INDONESIAN NEWS ARTICLES USING GRAPH CONVOLUTIONAL NETWORK
Author(s) -
Garmastewira Garmastewira,
Masayu Leylia Khodra
Publication year - 2019
Publication title -
journal of ict
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.217
H-Index - 10
eISSN - 2180-3862
pISSN - 1675-414X
DOI - 10.32890/jict2019.18.3.4675
Subject(s) - automatic summarization , computer science , sentence , natural language processing , artificial intelligence , graph , precision and recall , recurrent neural network , word (group theory) , word embedding , artificial neural network , theoretical computer science , embedding , mathematics , geometry
Multi-document summarization transforms a set of related documents into one concise summary. Existing Indonesian news articles summarizations do not take relationships between sentences into account and heavily depends on Indonesian language tools and resources. In this paper, we employ Graph Convolutional Network (GCN) which accepts word embedding sequence and sentence relationship graph as input for Indonesian news articles summarization. Our system is comprised of four main components, which are preprocess, graph construction, sentence scoring, and sentence selection components. Sentence scoring component is a neural network that uses Recurrent Neural Network (RNN) and GCN to produce the scores of all sentences. We use three different representation types for the sentence relationship graph. Sentence selection component then generates summary with two different techniques, which are by greedily choosing sentences with the highest scores and by using Maximum Marginal Relevance (MMR) technique. The evaluation shows that GCN summarizer with Personalized Discourse Graph (PDG) graph representation system achieves the best results with average ROUGE-2 recall score of 0.370 for 100-word summary and 0.378 for 200-word summary. Sentence selection using greedy technique gives better results for generating 100-word summary, while MMR performs better for generating 200-word summary.