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ICMCS
2007
IEEE
130views Multimedia» more  ICMCS 2007»
9 years 10 months ago
Word Topical Mixture Models for Extractive Spoken Document Summarization
This paper considers extractive summarization of Chinese spoken documents. In contrast to conventional approaches, we attempt to deal with the extractive summarization problem und...
Berlin Chen, Yi-Ting Chen
PRL
2008
181views more  PRL 2008»
9 years 3 months ago
Extractive spoken document summarization for information retrieval
The purpose of extractive summarization is to automatically select a number of indicative sentences, passages, or paragraphs from the original document according to a target summa...
Berlin Chen, Yi-Ting Chen
ACL
2009
9 years 1 months ago
Summarizing multiple spoken documents: finding evidence from untranscribed audio
This paper presents a model for summarizing multiple untranscribed spoken documents. Without assuming the availability of transcripts, the model modifies a recently proposed unsup...
Xiaodan Zhu, Gerald Penn, Frank Rudzicz
MM
2015
ACM
21views Multimedia» more  MM 2015»
3 years 11 months ago
SpeakerLDA: Discovering Topics in Transcribed Multi-Speaker Audio Contents
Topic models such as Latent Dirichlet Allocation (LDA) [3] have been extensively used for characterizing text collections according to the topics discussed in documents. Organizin...
Damiano Spina, Johanne R. Trippas, Lawrence Cavedo...
ICML
2006
IEEE
10 years 4 months ago
Pachinko allocation: DAG-structured mixture models of topic correlations
Latent Dirichlet allocation (LDA) and other related topic models are increasingly popular tools for summarization and manifold discovery in discrete data. However, LDA does not ca...
Wei Li, Andrew McCallum
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