• DocumentCode
    79830
  • Title

    PPSGen: Learning-Based Presentation Slides Generation for Academic Papers

  • Author

    Yue Hu ; Xiaojun Wan

  • Author_Institution
    MOE Key Lab. of Comput. Linguistics, Peking Univ., Beijing, China
  • Volume
    27
  • Issue
    4
  • fYear
    2015
  • fDate
    April 1 2015
  • Firstpage
    1085
  • Lastpage
    1097
  • Abstract
    In this paper, we investigate a very challenging task of automatically generating presentation slides for academic papers. The generated presentation slides can be used as drafts to help the presenters prepare their formal slides in a quicker way. A novel system called PPSGen is proposed to address this task. It first employs the regression method to learn the importance scores of the sentences in an academic paper, and then exploits the integer linear programming (ILP) method to generate well-structured slides by selecting and aligning key phrases and sentences. Evaluation results on a test set of 200 pairs of papers and slides collected on the web demonstrate that our proposed PPSGen system can generate slides with better quality. A user study is also illustrated to show that PPSGen has a few evident advantages over baseline methods.
  • Keywords
    abstracting; data mining; integer programming; learning (artificial intelligence); linear programming; regression analysis; ILP method; PPSGen; academic paper; integer linear programming; learning-based presentation slides generation; regression method; Data models; Feature extraction; Predictive models; Support vector machines; Training data; XML; Abstracting methods; text mining;
  • fLanguage
    English
  • Journal_Title
    Knowledge and Data Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1041-4347
  • Type

    jour

  • DOI
    10.1109/TKDE.2014.2359652
  • Filename
    6906256