TY - GEN
T1 - Discovering authoritative news sources and top news stories
AU - Hu, Yang
AU - Li, Mingjing
AU - Li, Zhiwei
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2006
Y1 - 2006
N2 - With the popularity of reading news online, the idea of assembling news articles from multiple news sources and digging out the most important stories has become very appealing. In this paper we present a novel algorithm to rank assembled news articles as well as news sources according to their importance and authority respectively. We employ the visual layout information of news homepages and exploit the mutual reinforcement relationship between news articles and news sources. Specifically, we propose to use a label propagation based semi-supervised learning algorithm to improve the structure of the relation graph between sources and new articles. The integration of the label propagation algorithm with the HITS like mutual reinforcing algorithm produces a quite effective ranking algorithm. We implement a system TOPSTORY which could automatically generate homepages for users to browse important news. The result of ranking a set of news collected from multiple sources over a period of half a month illustrates the effectiveness of our algorithm. © Springer-Verlag Berlin Heidelberg 2006.
AB - With the popularity of reading news online, the idea of assembling news articles from multiple news sources and digging out the most important stories has become very appealing. In this paper we present a novel algorithm to rank assembled news articles as well as news sources according to their importance and authority respectively. We employ the visual layout information of news homepages and exploit the mutual reinforcement relationship between news articles and news sources. Specifically, we propose to use a label propagation based semi-supervised learning algorithm to improve the structure of the relation graph between sources and new articles. The integration of the label propagation algorithm with the HITS like mutual reinforcing algorithm produces a quite effective ranking algorithm. We implement a system TOPSTORY which could automatically generate homepages for users to browse important news. The result of ranking a set of news collected from multiple sources over a period of half a month illustrates the effectiveness of our algorithm. © Springer-Verlag Berlin Heidelberg 2006.
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U2 - 10.1007/11880592_18
DO - 10.1007/11880592_18
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 3540457801
SN - 9783540457800
VL - 4182 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 230
EP - 243
BT - Information Retrieval Technology - Third Asia Information Retrieval Symposium, AIRS 2006, Proceedings
PB - Springer Verlag
T2 - 3rd Asia Information Retrieval Symposium, AIRS 2006
Y2 - 16 October 2006 through 18 October 2006
ER -