Automatic Summarization of Highly Spontaneous Speech

被引：4

作者：

Beke, Andras ^{[1
]}

Szaszak, Gyorgy ^{[2
]}

机构：

[1] Hungarian Acad Sci, Res Inst Linguist, Budapest, Hungary

[2] Budapest Univ Technol & Econ, Dept Telecommun & Media Informat, Budapest, Hungary

来源：

SPEECH AND COMPUTER | 2016年 / 9811卷

关键词：

Speech summarization; Latent semantic indexing; Spontaneous speech;

D O I：

10.1007/978-3-319-43958-7_16

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper addresses speech summarization of highly spontaneous speech. Speech is converted into text using an ASR, then segmented into tokens. Human made and automatic, prosody based tokenization are compared. The obtained sentence-like units are analysed by a syntactic parser to help automatic sentence selection for the summary. The preprocessed sentences are ranked based on thematic terms and sentence position. The thematic term is expressed in two ways: TF-IDF and Latent Semantic Indexing. The sentence score is calculated as linear combination of the thematic term score and a sentence position score. To generate the summary, the top 10 candidates for the most informative/best summarizing sentences are selected. The system performance showed comparable results (recall: 0.62, precision: 0.79 and F-measure 0.68) with the prosody based tokenization approach. A subjective test is also carried out on a Likert scale.

引用

页码：140 / 147

页数：8

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