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Automatic Extraction Of Useful Information From Food -Health Articles Related To Diabetes, Cardiovascular Disease And Cancer, Ken Sunong

Faculty of Applied Science and Technology - Exceptional Student Work, Applied Computing Theses

Food-health articles (FHA) contain invaluable information for health promotion. However, extracting this information manually is a challenging process due to the length and number of articles published yearly. Automatic text summarization efficiently identifies useful information across large bodies of text which in turn speeds up the delivery of useful information from FHA. This research work aims to investigate the performance of statistical based summarization and graphical based unsupervised learning summarization in extracting useful information from FHA related to diabetes, cardiovascular disease and cancer. Various combinations of introduction, result and conclusion sections of three hundred articles were collected, preprocessed and used …