Title
Classification and skimming of articles for an effective news browsing
Abstract
In order to browse the news video effectively, classification and skimming of news articles are positively essential. In this paper, we propose the classification and skimming of articles for an effective news browsing. The classification method uses tags to distinguish speakers in the closed-caption. The skimming method extracts the representative sentence from the part of article introduced by the anchor in the closed-caption and the representative frames consisting of anchor frame, open-caption frames, and frames synchronized with high-frequency terms. In the experiment, we have applied the proposed classification and skimming methods to news video with Korean closed-captions, and have empirically confirmed that the proposed methods could support effective browsing of news videos.
Year
DOI
Venue
2005
10.1007/11553939_100
KES (3)
Keywords
Field
DocType
anchor frame,classification method,representative frame,effective browsing,skimming method,effective news browsing,news video,proposed classification,news article,high frequency,frame synchronization
World Wide Web,Information retrieval,Computer science,Phrase,Sentence,Distributed computing
Conference
Volume
ISSN
ISBN
3683
0302-9743
3-540-28896-1
Citations 
PageRank 
References 
0
0.34
5
Authors
3
Name
Order
Citations
PageRank
Jungwon Cho14611.21
Seungdo Jeong2258.82
Byung-Uk Choi35014.62