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ICASSP
2008
IEEE
13 years 11 months ago
A comparative study of probabilistic ranking models for spoken document summarization
The purpose of extractive document summarization is to automatically select a number of indicative sentences, passages, or paragraphs from the original document according to a tar...
Shih-Hsiang Lin, Yi-Ting Chen, Hsin-Min Wang, Bin ...
ICMCS
2007
IEEE
130views Multimedia» more  ICMCS 2007»
13 years 11 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
SIGIR
2004
ACM
13 years 10 months ago
Focused named entity recognition using machine learning
In this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named entity recognition. We show that th...
Li Zhang, Yue Pan, Tong Zhang
BMCBI
2006
195views more  BMCBI 2006»
13 years 5 months ago
Hubs of knowledge: using the functional link structure in Biozon to mine for biologically significant entities
Background: Existing biological databases support a variety of queries such as keyword or definition search. However, they do not provide any measure of relevance for the instance...
Paul Shafer, Timothy Isganitis, Golan Yona