SISTEM PERINGKAS OTOMATIS ABSTRAKTIF DENGAN MENGGUNAKAN RECURRENT NEURAL NETWORK

Abstract

Abstractive Text Summarization try to creates a shorter version of a text while preserve its meaning. We try to use Recurrent Neural Network (RNN) to create summaries of Bahasa Indonesia text. We get corpus from Detik dan Kompas site news. We used word2vec to create word embedding from our corpus then train our data set with RNN to create a model. This model used to generate news. We search the best model by changing word2vec size and RNN hidden states. We use system evaluation and Q&A Evaluation to evaluate our model. System evaluation showed that model with 6457 data set, 200 word2vec size, and 256 RNN hidden states gives best accuracy for 99.8810%. This model evaluated by Q&A Evaluation. Q&A Evaluation showed that the model gives 46.65% accurary.