自然語言處理的模型由於需要準備字典給模型做挑選，因此衍生出 Out Of Vocabulary(OOV) 這個問題，是指句子裡面有不存在於字典的用詞，過往有人嘗試在 Recurrent Neural Networks(RNN) 上加入複製機制，以改善這個問題。但 Transformer 是自然語言處理的新模型，不若過往的 RNN 或 Convolutional Neural Networks(CNN) 已經有許多改善機制，因此本研究將針對 Transformer 進行改良，添加額外的輸入和輸 出的相互注意力，來完成複製機制的概念，讓 Transformer 能有更佳的表現。;In natural language processing, Out of Vocabulary(OOV) has always been a issue. It limits the performance of summarization model. Past study resolve this problem by adding copy mechanism to Recurrent Neural Networks(RNN). However, resent study discover a new model – Transformer which outperforms RNN in many categories. So, this work will improve Transformer model by adding copy mechanism in order to enhance the relation of model’s input and output result..
