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The effectiveness of automatic text summarization in mobile learning contexts

The effectiveness of automatic text summarization in mobile learning contexts. Presenter : Yu-Ting LU Authors: Guangbing Yang , Nian-Shing Chen , Kinshuk , Erkki Sutinen , Terry Anderson , Dunwei Wen 2013. CE. Outlines. Motivation Objectives Methodology Experiments Conclusions

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The effectiveness of automatic text summarization in mobile learning contexts

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  1. The effectiveness of automatic text summarization in mobile learning contexts Presenter: Yu-Ting LUAuthors: Guangbing Yang , Nian-Shing Chen , Kinshuk , ErkkiSutinen , Terry Anderson ,DunweiWen2013. CE

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • Reducing the amount of content transmitted may negatively impact the meaning conveyed within. • Due to the problem of the oft-decried information overload, delivering large amounts of text contents makes mobile learners challenging, especially for learning purposes.

  4. Objectives • This study investigates automatic text summarization to provide a tool set that reduces the quantity of textual content for mobile learning support. • This study aims to investigate a technology for content processing that can be used to summarize text contents effectively to align content size to match various characteristics of mobile devices.

  5. Methodology

  6. Methodology – Research questions • Identifies the general usefulness of the generated summaries for learning purposes. • Determines what the optimal summaries will be if a higher level of learning achievement is required. • Analyzes what kind of short summaries are still helpful in reaching a sufficient level of learning.

  7. 2 office clerks • 3 customer service representatives • 2 office clerks • 3 customer service representatives Methodology – Participants • 2 office clerks • 3 customer service representatives • 2 office clerks • 3 customer service representatives • 2 office clerks • 3 customer service representatives

  8. Methodology – The system M

  9. Methodology – Experimental dataset & task

  10. Methodology – Experimental dataset & task

  11. Methodology – Experimental treatments

  12. Experiments

  13. Experiments

  14. Experiments

  15. Experiments

  16. Conclusions • This summarization approach is able to generate summaries effectively from learning contents. • This study has the following limitations that could be addressed in future research. • Sample size • Different backgrounds • Semantic differences or similarities

  17. Comments • Advantages • Generating summaries effectively • Applications • Automatic text summarization • Mobile learning

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