Mandarin Chinese broadcast news retrieval and summarization using probabilistic generative models

Hsin Min Wang, Berlin Chen

Research output: Contribution to conferencePaper

Abstract

This paper presents our recent research work on applying probabilistic generative models to Mandarin Chinese broadcast news retrieval and summarization. Most models can be trained in either a supervised or unsupervised manner. In addition, both literal term matching and concept matching strategies have been intensively investigated. This paper also presents a prototype web-based Mandarin Chinese broadcast news retrieval system, which is based on technologies such as automatic story segmentation, automatic speech recognition, spoken document retrieval and summarization.

Original languageEnglish
Pages331-337
Number of pages7
Publication statusPublished - 2009 Dec 1
EventAsia-Pacific Signal and Information Processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009 - Sapporo, Japan
Duration: 2009 Oct 42009 Oct 7

Other

OtherAsia-Pacific Signal and Information Processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009
CountryJapan
CitySapporo
Period09/10/409/10/7

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Electrical and Electronic Engineering
  • Communication

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  • Cite this

    Wang, H. M., & Chen, B. (2009). Mandarin Chinese broadcast news retrieval and summarization using probabilistic generative models. 331-337. Paper presented at Asia-Pacific Signal and Information Processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009, Sapporo, Japan.