Spoken document summarization using topic-related corpus and semantic dependency grammar

被引：0

作者：

Hsieh, CH ^{[1
]}

Huang, CL ^{[1
]}

Wu, CH ^{[1
]}

机构：

[1] Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan 70101, Taiwan

来源：

2004 INTERNATIONAL SYMPOSIUM ON CHINESE SPOKEN LANGUAGE PROCESSING, PROCEEDINGS | 2004年

关键词：

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：

This study presents a spoken document summarization scheme using a topic-related corpus and semantic dependency grammars. The summarization score considers speech recognition confidence, word significance, word trigram, semantic dependency grammar (SDG) and probabilistic context free grammar (PCFG). In addition, a topic-related corpus consisting of keywords as well as article is used to estimate the word significance score using latent semantic indexing (LSI). Semantic relations between words are determined by SDG using HowNet and Sinica Treebank. The dynamic programming algorithm is applied to decide the summarization ratio and look for the best summarization result according to summarization scores. Experimental results indicate that the proposed approach effectively extracts important words with semantic dependency and gives a promising speech summary.

引用

页码：333 / 336

页数：4

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