<![CDATA[In the era of Industry 4.0, information has become a primary necessity for society today as it enables people to know about various current events worldwide. With the rapid development of information technology and the internet, there has been an abundance of documents available that we can search according to our needs. Text Summarization Machines have the function of presenting essential information from the original documents in a shorter format while still preserving the main content and helping users understand the information from lengthy documents faster. In this case, the method used is the Text Summarization Feature-Based approach, utilizing the Backpropagation Artificial Neural Network algorithm for sentence prediction calculations. The Backpropagation Artificial Neural Network algorithm seeks the most optimal weights during its process. In the testing process with five document samples, the final result obtained was a text summary model that could predict the overall number of labels correctly. However, it struggled in predicting which ones should be labeled as "true" and which ones should be labeled as "false".]]>
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