Penerapan Recurrent Neural Network untuk Pembuatan Ringkasan Ekstraktif Otomatis pada Berita Berbahasa Indonesia

Abstract

Technology advancement in modern world allow huge amount of information to flow everyday and news became one of the source to get that information. Because of this advancement, available information through news have been increased and so program is develop to make summary of news to reduce reading time using the neural network as the basis of this program.

The method used for training the model is Recurrent Neural Network. The type of Recurrent Neural Network that being used is Gated Recurrent Unit that is run in 2 level, the word level and then the sentence level. As for making the Recurrent Neural Network model, some experiment can be carried out, like changing initial weight of the word embedding, change the pooling method, removing dropout layer, and some preprocessing for the dataset.

The results shows that for the initial model, F1 – Score for ROUGE – 1, ROUGE – 2, and ROUGE – L can reach up to 80% when using extractive summary as the reference and up to 50% when using abstractive summary as the reference. The experiment shows that the best model is using training dataset as the initial word embedding weight, using average pooling and removing the dropout layer. The best experiment result gives F1 – Score 84.10 for ROUGE – 1, 83.10 for ROUGE – 2 and 83.31 for ROUGE – L using the extractive reference and 57.01 for ROUGE – 1, 51.17 for ROUGE – 2 and 55.10 for ROUGE – L using the abstrative reference.