| dc.contributor.author | Muia, Charles M. | |
| dc.contributor.author | Oirere, Aaron M. | |
| dc.contributor.author | Ndungu, Rachel N. | |
| dc.date.accessioned | 2024-04-24T13:06:05Z | |
| dc.date.available | 2024-04-24T13:06:05Z | |
| dc.date.issued | 2024-04 | |
| dc.identifier.citation | International Journal of Advanced Trends in Computer Science and Engineering,13(2), March - April 2024, 37- 43 | en_US |
| dc.identifier.issn | 2278-3091 | |
| dc.identifier.uri | https://doi.org/10.30534/ijatcse/2024/011322024 | |
| dc.identifier.uri | http://www.warse.org/IJATCSE/static/pdf/file/ijatcse011322024.pdf | |
| dc.identifier.uri | http://repository.mut.ac.ke:8080/xmlui/handle/123456789/6437 | |
| dc.description.abstract | Transformer-based models such as GPT, T5, BART, and PEGASUS have made substantial progress in text summarization, a sub-domain of natural language processing that entails extracting important information from lengthy texts. The main objective of this research was to conduct a comparative analysis of these four transformer-based models based on their performance in text summarization of news articles. In achieving this objective, the transformer models pre-trained on extensive datasets were fine-tuned on the CNN/DailyMail dataset using a low learning rate to preserve the learned representations. The T5 transformer records the highest scores of 35.12, 22.75, 32.82, and 28.59 in ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-Lsum respectively, surpassing GPT, BART, and PEGASUS across all ROUGE metrics. The findings deduced from this study establish the proficiency of encoder-decoder models such as T5 in summary generation. Furthermore, the findings also demonstrated that the fine-tuning process's effectiveness in pre-trained models is improved when the pre-training objective closely aligns with the downstream task. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | International Journal of Advanced Trends in Computer Science and Engineering | en_US |
| dc.subject | Natural Language Processing, ROUGE Metrics, Text Summarization, Transformers. | en_US |
| dc.title | A Comparative Study of Transformer-based Models for Text Summarization of News Articles | en_US |
| dc.type | Article | en_US |
