| 한글제목(Korean Title) |
Improving Abstractive Summarization by Training Masked Out-of-Vocabulary Words |
| 영문제목(English Title) |
Improving Abstractive Summarization by Training Masked Out-of-Vocabulary Words |
| 저자(Author) |
Tae-Seok Lee
Hyun-Young Lee
Seung-Shik Kang
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| 원문수록처(Citation) |
VOL 18 NO. 03 PP. 0344 ~ 0358 (2022. 06) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Text summarization is the task of producing a shorter version of a long document while accurately preserving the main contents of the original text. Abstractive summarization generates novel words and phrases using a language generation method through text transformation and prior-embedded word information. However, newly coined words or out-of-vocabulary words decrease the performance of automatic summarization because they are not pre-trained in the machine learning process. In this study, we demonstrated an improvement in summarization quality through the contextualized embedding of BERT with out-of-vocabulary masking. In addition, explicitly providing precise pointing and an optional copy instruction along with BERT embedding, we achieved an increased accuracy than the baseline model. The recall-based word-generation metric ROUGE-1 score was 55.11 and the word-order-based ROUGE-L score was 39.65. |
| 키워드(Keyword) |
BERT
Deep Learning
Generative Summarization
Selective OOV Copy Model
Unknown Words
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| 파일첨부 |
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