基于关键体词抽取的韩国语自动文摘  

Korean Automatic Summarization Based on Key-noun Extraction

作　　者：王琳 刘伍颖 WANG Lin;LIU Wuying(Xianda College of Economics and Humanities,Shanghai International Studies University,Shanghni 200083,China;laboratory of language Engineering and Computing,Guangdong University of Foreign Studies,Guangzhou Guangdong 510420, China;Engineering Research Center for Cyberspace Content Security, Guangdong University of Foreign Studies,Guangzhou,Guangdong 510420,China)

机构地区：[1]上海外国语大学贤达经济人文学院,上海200083 [2]广东外语外贸大学语言工程与计算实验室,广东广州510420 [3]广东外语外贸大学网络空间内容安全工程技术研究中心,广东广州510420

出　　处：《中文信息学报》2019年第6期50-56,共7页Journal of Chinese Information Processing

基　　金：国家语委重点项目(ZDI135-26);广东省自然科学基金(2018A030313672);广东省高校特色创新项目(2015KTSCX035);广东省哲学社会科学重点实验室招标项目(LEC2017WTKT002);广州市人文社科重点研究基地(广州国际城市创新传播研究中心)重点项目(2017-IC-02)

摘　　要：非通用语言信息爆炸导致人们的时间更加稀缺且注意力更加发散。该文围绕韩国语文本的自动文摘问题,提出一种新的基于关键体词抽取的韩国语文摘算法。该文认为韩国语体词主要表示语义信息,而韩国语谓词更多地担负句法框架功能。实验结果表明基于关键体词抽取的文摘算法效果优于采用谓词或全词的效果,且新提出的韩国语文摘算法在韩国语文摘任务中能够达到最优性能,证明了体词主要表示语义信息的论断是有效的。This paper addresses the issue of automatic summarization for Korean texts and presents a novel Korean summarization(KKS)method based on key-noun extraction.We deem that Korean nouns mainly represent semantic information,while Korean predicates are more responsible for syntactic frame function.The experimental results show that the performance of our KKS algorithm is better than that of predicate-based one or all-word-based one,and the KKS algorithm can achieve the best performance in the Korean summarization task,which also proves the effectiveness of our assertion for the semantic function of Korean nouns.

关 键 词：自动文摘 韩国语 体词 谓词 ROUGE 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]