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

Hind Bitar, Amal Babour, Fatema Nafa, Ohoud Alzamzami and Sarah Alismail
Additional contact information
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

IJERPH, 2022, vol. 19, issue 13, 1-15

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.

Keywords: BERT; cervical cancer; HPV; machine learning; text summarization (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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