RP192012
Text Summarization by Machine Learning for News Articles

Text summarization is a technique to generate a text summary with a more abstract, condensed knowledge structures by a natural language text understanding system. Extractive text summarization is a technique to extract the essence sentences of a long text article to produce a summary. Several machine learning models for extractive text summarization are proposed in current studies. However, there is a lack of researches to compare the performance of these machine learning models in this field. There are also many techniques to preprocess the textual data for machine learning. This research helps to identify the champion machine learning model in text summarization for news articles and to identify the best text preprocessing method in the machine learning of text summarization. CNN/Daily Mail database is used for the comparative study of text summarization in this research.

Name: Hew Zi Jian

Matrics No: 132913

Email: zijian@student.usm.my

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Supervisor: Dr. Chew Xinying

Email: xinying@usm.my