Paper Info
Title
A front-page news-selection algorithm based on topic modelling using raw text
Abstract
Front-page news selection is the task of finding important news articles in news aggregators. In this study, we examine news selection for public front pages using raw text, without any meta-attributes such as click counts. A novel algorithm is introduced by jointly considering the importance and diversity of selected news articles and the length of front pages. We estimate the importance of news, based on topic modelling, to provide the required diversity. Then we select important documents from important topics using a priority-based method that helps in fitting news content into the length of the front page. A user study is subsequently conducted to measure effectiveness and diversity, using our newly-generated annotation program. Annotation results show that up to seven of 10 news articles are important and up to nine of them are from different topics. Challenges in selecting public front-page news are addressed with an emphasis on future research.
Year
DOI
Venue
2015
10.1177/0165551515589069
Journal of Information Science
Keywords
Field
DocType
Diversity, document importance, front page, LDA, news selection, priority scheduling, topic importance, topic modelling
Data mining,World Wide Web,Annotation,Information retrieval,News aggregator,Computer science,Selection algorithm,Priority scheduling,Topic model
Journal
Volume
Issue
ISSN
41
5
0165-5515
Citations 
PageRank 
References 
2
0.85
12
Authors
2
Name
Order
Citations
PageRank
Cagri Toraman152.03
Fazli Can258194.63