@article{M978F0227,
title = "Unsupervised Abstractive Summarization Method that Suitable
for Documents with Flows",
journal = "KIPS Transactions on Software and Data Engineering",
year = "2021",
issn = "2287-5905",
doi = "https://doi.org/10.3745/KTSDE.2021.10.11.501",
author = "Hoon-suk Lee/Soon-hong An/Seung-hoon Kim",
keywords = "NLP, Summarization, GAN, BERT, Transformer",
abstract = "Recently, a breakthrough has been made in the NLP area by Transformer techniques based on encoder-decoder. However, this only
can be used in mainstream languages where millions of dataset are well-equipped, such as English and Chinese, and there is a limitation
that it cannot be used in non-mainstream languages where dataset are not established. In addition, there is a deflection problem that
focuses on the beginning of the document in mechanical summarization. Therefore, these methods are not suitable for documents with
flows such as fairy tales and novels. In this paper, we propose a hybrid summarization method that does not require a dataset and improves
the deflection problem using GAN with two adaptive discriminators. We evaluate our model on the CNN/Daily Mail dataset to verify
an objective validity. Also, we proved that the model has valid performance in Korean, one of the non-mainstream languages."
}