Information explosion causes a serious scarcity of people's time and a severe divergence of people's attention。 This paper addresses the issue of automatic summarization for Korean texts and presents a novel keyword-extraction-based Korean text summarization (KKTS) algorithm。 We investigate the enhancement of POS-tagging to the KKTS algorithm according to three kinds of text feature: noun words， predicate words， and all words。 The experimental results show that our POS-tagging enhanced KKTS algorithm according to noun words can achieve the best performance in the Korean summarization task。
Korean text summarizationPOS-taggingKeyword extractionNoun wordsROUGE
Wuying Liu、Lin Wang
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Laboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou 510420, Guangdong, China
Center for Translation Studies, Guangdong University of Foreign Studies, Guangzhou 510420, Guangdong, China
International conference on intelligent computing methodologies
NETL
Springer Nature
