A Review on Learning Based Automatic PPT Generation Using Machine Learning

Authors

  • Abhineet Ranjan  BE, Department of Computer Engineering, AISSMS College of Engineering, Pune, Maharashtra, India
  • Akash Gangadhare   BE, Department of Computer Engineering, AISSMS College of Engineering, Pune, Maharashtra, India
  • S. V. Shinde  Assistant Professor, Department of Computer Engineering, AISSMS College of Engineering, Pune, Maharashtra, India

Keywords:

Classification, NLP, Support Vector Regression (SVR), ILP, Slide Generation, NLTK, Feature extraction.

Abstract

Presentation slides are widely used to communicate information to the audience. There are various tools available in the market which only deals with formatting of the slides but not the content. However, this traditional way of preparing slides is labour-intensive in nature and leaves scope for human errors. Also, for lengthy documents there is a chance of some important information being missed out. The drawbacks of the traditional way lead to need for intelligent system. The intelligent system needs to be capable of generating slides with minimum human interference. In this paper, we are enforcing the automated PPT creation from multi-documents of different extensions based on input query or title that formulate extraction of valuable information source and model a presentation view to automate slide creation using integer linear programming (ILP) method to generate well-structured slides by selecting and aligning key phrases and sentences. This will eventually help in reducing a great amount of the presenter’s time and efforts. The proposed system works on natural language processing (NLP) rules to classify data for the desired slides.

References

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Published

2018-02-28

Issue

Section

Research Articles

How to Cite

[1]
Abhineet Ranjan, Akash Gangadhare , S. V. Shinde, " A Review on Learning Based Automatic PPT Generation Using Machine Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.547-552, March-April-2018.