Given the rapid development of communication technology, online business websites are becoming increasingly popular. However, it is time-consuming for customers to read long product reviews. Therefore, if we can generate questions that can be answered by extensive product specifications, customers could obtain their desired information easily. In this thesis, we aim to tackle the automatic question generation task. We proposed a novel network Adaptive Copying Neural Network (ACNN). The proposed model adds a copying mechanism component onto a bidirectional LSTM architecture to adaptively generate more suitable questions from the input data. Subsequently, we calculated the generated questions' summarization score to see if they could be answered by the reviews. In our evaluation experiments, we confirmed that our method can outperform baseline QG methods in terms of BLEU, ROUGE and human evaluation scores. In addition, we combined the summarization scores with our model, which resulted in a performance boost.