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Analyzing the Impact of Sequential Context Learning on the Transformer Based Korean Text Summarization Model
Subin Kim, Yongjun Kim, Junseong Bang
http://doi.org/10.5626/JOK.2021.48.10.1097
Text summarization reduces the sequence length while maintaining the meaning of the entire article body, solving the problem of overloading information and helping readers consume information quickly. To this end, research on a Transformer-based English text summarization model has been actively conducted. Recently, an abstract text summary model reflecting the characteristics of English with a fixed word order by adding a Recurrent Neural Network (RNN)-based encoder was proposed. In this paper, we study the effect of sequential context learning on the text abstract summary model by using an RNN-based encoder for Korean, which has more free word order than English. Transformer-based model and a model that added RNN-based encoder to existing Transformer model are trained to compare the performance of headline generation and article body summary for the Korean articles collected directly. Experiments show that the model performs better when the RNN-based encoder is added, and that sequential contextual information learning is required for Korean abstractive text summarization.
Dimensional Sentiment Analysis of Korean Text using Data Balancing
http://doi.org/10.5626/JOK.2021.48.7.790
Compared with most studies on categorical sentiment analysis which aims to represent emotional states as a small set of emotion categories, there have been fewer studies on dimensional sentiment analysis which treats sentiment analysis as a regression problem because of the shortage of data. Recently, the National Information Society Agency (NIA) released open data, Multimodal Video Data, through their web site, AI Hub. Using this data, we experimented with dimensional sentiment analysis of Korean text. For this purpose, we used CNN which is one of the conventional deep learning models in NLP. We also verified that data balancing could improve the performance of models. The results show that the model trained on Multimodal Video Data performs well enough to show that the data should be useful for dimensional sentiment analysis of Korean text and that with data balancing the model can perform better in spite of their fewer training data.
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