IMPROVING TEXT SUMMARIZATION QUALITY BY COMBINING T5-BASED MODELS AND CONVOLUTIONALSEQ2SEQ MODELS

Abstract

Within the field of natural language processing, there are several subfields that are closely related to information retrieval, including the subfield of automatic text summarization. This research utilizes the T5 and Seq2Seq convolutional models via the Hugging Face platform to improve the quality of text summaries. This research identifies important features that influence text summary results, such as the use of uppercase and lowercase letters that influence the understanding of document content. To optimize the model, this research adjusts parameters involving layer dimensions, learning rate, batch size, and Dropout implementation to avoid overfitting. Evaluation of model performance is carried out using the comprehensive ROUGE metric. The results of this research show promising results, with ROUGE-1 values reaching an average of 0.8 on the four documents tested, reflecting optimal performance. Similarly, ROUGE-2 recorded an average of 0.83, which also reflects optimal results. Furthermore, ROUGE-L also achieved an average of 0.8 for the text summary model on the four documents evaluated, indicating optimal performance.