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      • Faculty of Engineering
      • Department of Computer Engineering
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      A front-page news-selection algorithm based on topic modelling using raw text

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      Author
      Toroman, C.
      Can, F.
      Date
      2015
      Source Title
      Journal of Information Science
      Print ISSN
      0165-5515
      Electronic ISSN
      1741-6485
      Publisher
      Sage Publications Ltd.
      Volume
      41
      Issue
      5
      Pages
      676 - 685
      Language
      English
      Type
      Article
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      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.
      Keywords
      Diversity
      Document importance
      Front page
      LDA
      News selection
      Priority scheduling
      Topic importance
      Topic modelling
      Permalink
      http://hdl.handle.net/11693/48286
      Published Version (Please cite this version)
      https://journals.sagepub.com/doi/pdf/10.1177/0165551515589069
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      • Department of Computer Engineering 1308

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