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Korean Text Summarization using MASS with Copying and Coverage Mechanism and Length Embedding
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MASS와 복사 및 커버리지 메커니즘과 길이 임베딩을 이용한 한국어 문서 요약

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Type
Academic journal
Author
Youngjun Jung (강원대학교) Changki Lee (강원대학교) Wooyoung Go (국가보안기술연구소) Hanjun Yoon (국가보안기술연구소)
Journal
Korean Institute of Information Scientists and Engineers Journal of KIISE Vol.49 No.1 KCI Excellent Accredited Journal
Published
2022.1
Pages
25 - 31 (7page)
DOI
10.5626/JOK.2022.49.1.25

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Korean Text Summarization using MASS with Copying and Coverage Mechanism and Length Embedding
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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.
#문서 요약 #사전학습 #복사 메커니즘 #커버리지 메커니즘 #길이 임베딩 #text summarization #pre-training #MASS #copying mechanism #coverage mechanism #length embedding

Contents

요약
Abstract
1. 서론
2. 관련 연구
3. 문서 요약 모델
4. 실험
5. 결론
References

References (8)

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Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio, "Neural Machine Translation by Jointly Learning to Align and Translate," ICLR, 2015. google schola Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, "Attention Is All You Need," NIPS, 2017. google schola Abigail See, Peter J. Liu, Christopher D. Manning, "Get To The Point: Summarization with Pointer-Generator Networks," arXiv preprint arXiv:1704.04368, 2017. Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," arXiv preprint arXiv:1810.04805, 2018. Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu, "MASS: Masked Sequence to Sequence Pre-training for Language Generation," ICML, 2019. google schola

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