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Korean Text Summarization using MASS with Copying and Coverage Mechanism and Length Embedding
Youngjun Jung, Changki Lee, Wooyoung Go, Hanjun Yoon
http://doi.org/10.5626/JOK.2022.49.1.25
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.
Topic Centric Korean Text Summarization using Attribute Model
Su-Hwan Yoon, A-Yeong Kim, Seong-Bae Park
http://doi.org/10.5626/JOK.2021.48.6.688
Abstractive summarization takes original text as an input and generates a summary containing the core-information about the original text. The abstractive summarization model is mainly designed by the Sequence-to-Sequence model. To improve quality as well as coherence of summary, the topic-centric methods which contain the core information of the original text are recently proposed. However, the previous methods perform additional training steps which make it difficult to take advantage of the pre-trained language model. This paper proposes a topic-centric summarizer that can reflect topic words to a summary as well as retain the characteristics of language model by using PPLM. The proposed method does not require any additional training. To prove the effectiveness of the proposed summarizer, this paper performed summarization experiments with Korean newspaper data.
English-Korean Neural Machine Translation using MASS with Relative Position Representation
Youngjun Jung, Cheoneum Park, Changki Lee, Junseok Kim
http://doi.org/10.5626/JOK.2020.47.11.1038
Neural Machine Translation has been mainly studied for a Sequence-to-Sequence model using supervised learning. However, since the supervised learning method shows low performance when the data is insufficient, recently, a transfer learning method of fine-tuning using the pre-training model based on a large amount of monolingual data such as BERT and MASS has been mainly studied in the field of natural language processing. In this paper, MASS using the pre-training method for language generation, was applied to the English-Korean machine translation. As a result of the experiment, the performance of the English-Korean machine translation model using MASS showed better performance than the existing models, and the performance of the machine translation model was further improved by applying the relative position representation method to MASS.
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