Text generation is one of the most important tasks in NLP and has been applied in many applications such as machine translation, question answering and text summarization. Most of recent studies on text generation use only the input for output generation. In this research we suggest that topic information of an input document is an important factor for generating the destination text. We will propose a deep neural network model in which we use topic information together with the input text for generating summarized texts. The experiment on Vietnamese news corpus shows that our model outperforms a baseline model at least 23% in BLEU score.
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