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

Chia Hsin Hsieh, Chien Lin Huang, Chung Hsien Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2004 International Symposium on Chinese Spoken Language Processing - Proceedings
Pages333-336
Number of pages4
Publication statusPublished - 2004 Dec 1
Event2004 International Symposium on Chinese Spoken Language Processing - Hong Kong, China, Hong Kong
Duration: 2004 Dec 152004 Dec 18

Publication series

Name2004 International Symposium on Chinese Spoken Language Processing - Proceedings

Other

Other2004 International Symposium on Chinese Spoken Language Processing
CountryHong Kong
CityHong Kong, China
Period04-12-1504-12-18

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'Spoken document summarization using topic-related corpus and semantic dependency grammar'. Together they form a unique fingerprint.

Cite this