EFFICIENT KOREAN TEXT SUMMARIZATION BASED ON KEY PHRASE EXTRACTION

被引：0

作者：

Liu, Wuying ^{[1
]}

Wang, Lin ^{[2
]}

机构：

[1] Guangdong Univ Foreign Studies, Lab Language Engn & Comp, Guangzhou 510420, Guangdong, Peoples R China

[2] Guangdong Univ Foreign Studies, Ctr Translat Studies, Guangzhou 510420, Guangdong, Peoples R China

来源：

PROCEEDINGS OF 2017 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1 | 2017年

关键词：

Korean Text Summarization; Multi-plugin Framework; Key Phrase Extraction; Maximal Marginal Relevance; ROUGE;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Language big data explosion causes a severe divergence of people's attention and a serious scarcity of people's time. This paper addresses the problem of Korean text summarization (KTS) and presents a flexible multi-plugin framework. Within the framework, we design a novel KTS algorithm based on key phrase extraction. Supported by the pluggable components of word stemming and part of speech tagging, the key-phrase-extraction-based KTS algorithm can complete text summarization efficiently. The experimental results show that our KTS algorithm with MMR plugin component can achieve the perfect performance in the Korean summarization task.

引用

页码：61 / 66

页数：6

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