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

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.