基于关键体词抽取的韩国语自动文摘

Korean Automatic Summarization Based on Key-noun Extraction

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.