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
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;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
DOI :
10.1109/TKDE.2014.2359652