Bengali News Abstractive Summarization: T5 Transformer and Hybrid Approach

In today's fast-paced world, everyone wants things to happen quickly. Thanks to the internet, news spreads super fast. But not all news is important. News summarization helps by giving a short version of each news story, so readers can easily figure out what type of news they want to read. There are two main types of summarization: Abstractive Text Summarization and Extractive Text Summarization. The process of abstractive text summarization is much more complex than that of extractive text summarization. This study proposes a model for generating extractive summaries, which are then utilized as input to generate abstractive summaries. The model uses the Bengali Text Summarization (BenSumm) model for extractive summarization and the Bangla Text-to-Text Transfer Transformer (BanlaT5) for abstractive summarization. The research also compares summarization acquired straight from the BanglaT5 model with summarization obtained via the proposed model. Abstractive summarization in the Bengali language has been accomplished using the Text-to-Text Transfer Transformer(T5) in this research. Although abstractive summarization of the Bengali language has been accomplished over the years using a variety of techniques, the field of using T5 in this field has only recently been discovered, and there is still a wide range of opportunities to be explored. The study has achieved promising results.