TY  - JOUR
T1  - Korean Text Summarization using MASS with Relative Position Representation
AU  - Jung, Youngjun 
AU  - Hwang, Hyunsun 
AU  - Lee, Changki 
JO  - Journal of KIISE, JOK
PY  - 2020
DA  - 2020/1/14
DO  - 10.5626/JOK.2020.47.9.873
KW  - text summarization
KW  - pre-training
KW  - relative position representation
AB  - In the language generation task, deep learning-based models that generate natural languages using a Sequence-to-Sequence model are actively being studied. In the field of text summarization, wherein the method of extracting only the core sentences from the text is used, an abstract summarization study is underway. Recently, a transfer learning method of fine-tuning using pre-training model based on large amount of monolingual data such as BERT and MASS has been mainly studied in the field of natural language processing. In this paper, after pre-training for the Korean language generation using MASS, it was applied to the summarization of the Korean text. As a result of the experiment, the Korean text summarization model using MASS was higher performance than the existing models. Additionally, the performance of the text summarization model was improved by applying the relative position representation method to MASS.