Jaeyeun Yoon  Ig-hoon Lee  Sang-goo Lee

10.5626/JOK.2020.47.2.180

Document Summarization Sequence-to-Sequence Model Attention Mechanism memory network Artificial neural network

Automatic document summarization is a task that generates the document in a suitable form from an existing document for a certain user or occasion. As use of the Internet increases, the various data including texts are exploding and the value of document summarization technology is growing. While the latest deep learning-based models show reliable performance in document summarization, the problem is that performance depends on the quantity and quality of the training data. For example, it is difficult to generate reliable summarization with existing models from the product review text of online shopping malls because of typing errors and grammatically wrong sentences. Online malls and portal web services are struggling to solve this problem. Thus, to generate an appropriate document summary in poor condition relative to quality and quantity of the product review learning data, this study proposes a model that generates product review summaries with additional information. We found through experiments that this model showed improved performances in terms of relevance and readability than the existing model for product review summaries.