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MASS와 복사 및 커버리지 메커니즘과 길이 임베딩을 이용한 한국어 문서 요약
정영준(Youngjun Jung),이창기(Changki Lee),고우영(Wooyoung Go),윤한준(Hanjun Yoon) Korean Institute of Information Scientists and Eng 2022 정보과학회논문지 Vol.49 No.1
Text summarization is a technology that generates a summary including important and essential information from a given document, and an end-to-end abstractive summarization model using a sequence-to-sequence model is mainly studied. Recently, a transfer learning method that performs fine-tuning using a pre-training model based on large-scale monolingual data has been actively studied in the field of natural language processing. In this paper, we applied the copying mechanism method to the MASS model, conducted pre-training for Korean language generation, and then applied it to Korean text summarization. In addition, coverage mechanism and length embedding were additionally applied to improve the summarization model. As a result of the experiment, it was shown that the Korean text summarization model, which applied the copying and coverage mechanism method to the MASS model, showed a higher performance than the existing models, and that the length of the summary could be adjusted through length embedding.