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Increasing Women’s Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study

Author

Listed:
  • Hind Bitar

    (Information Systems Department, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia)

  • Amal Babour

    (Information Systems Department, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia)

  • Fatema Nafa

    (Computer Science Department, Salem State University, Salem, MA 01970, USA)

  • Ohoud Alzamzami

    (Computer Science Department, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia)

  • Sarah Alismail

    (Center for Information Systems and Technology, Claremont Graduate University, Claremont, CA 91711, USA
    Beckman Research Institute, City of Hope, Duarte, CA 91010, USA)

Abstract

Despite the availability of online educational resources about human papillomavirus (HPV), many women around the world may be prevented from obtaining the necessary knowledge about HPV. One way to mitigate the lack of HPV knowledge is the use of auto-generated text summarization tools. This study compares the level of HPV knowledge between women who read an auto-generated summary of HPV made using the BERT deep learning model and women who read a long-form text of HPV. We randomly assigned 386 women to two conditions: half read an auto-generated summary text about HPV ( n = 193) and half read an original text about HPV ( n = 193). We administrated measures of HPV knowledge that consisted of 29 questions. As a result, women who read the original text were more likely to correctly answer two questions on the general HPV knowledge subscale than women who read the summarized text. For the HPV testing knowledge subscale, there was a statistically significant difference in favor of women who read the original text for only one question. The final subscale, HPV vaccination knowledge questions, did not significantly differ across groups. Using BERT for text summarization has shown promising effectiveness in increasing women’s knowledge and awareness about HPV while saving their time.

Suggested Citation

  • Hind Bitar & Amal Babour & Fatema Nafa & Ohoud Alzamzami & Sarah Alismail, 2022. "Increasing Women’s Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study," IJERPH, MDPI, vol. 19(13), pages 1-15, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:8100-:d:853973
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    References listed on IDEAS

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    1. Taejin Kim & Yeoil Yun & Namgyu Kim, 2021. "Deep Learning-Based Knowledge Graph Generation for COVID-19," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
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    Cited by:

    1. Bowen Zhang & Jinping Lin & Man Luo & Changxian Zeng & Jiajia Feng & Meiqi Zhou & Fuying Deng, 2022. "Changes in Public Sentiment under the Background of Major Emergencies—Taking the Shanghai Epidemic as an Example," IJERPH, MDPI, vol. 19(19), pages 1-20, October.

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