Enhancing Text Summarization with a T5 Model and Bayesian Optimization

Abstract

At present the habits and interests of individuals in obtaining information by reading large amounts of information have changed at the stage of reading information more concisely, but these changes have challenges such as the nature of the data which is still unstructured making it difficult to summarize text. This study applies a data cleaning process with text processing and manually annotates to divide the data into summary data and text data so that it can be used for the process of implementing the T5 model and Bayesian optimization. In the implementation of Bayesian optimization using the prior distribution and likelihood parameters. In implementing the T5 model there will be several stages such as processing training and test data then Decodification and Post-Processing processes. The results of this study were obtained using the ROUGE evaluation technique which resulted in an increased evaluation value. The T5 model produces a ROUGE 1 value with an average value of 0.42, ROUGE-2 has a value of 0.55 and ROUGE-L has a value of 0.46 while applying Bayesian optimization produces a ROUGE-1 evaluation with an average value of 0.53 ROUGE-2 has a value of 0.55 and ROUGE-L has a value of 0.59.