Text Summarization Using BART Transformer Model This project demonstrates text summarization using the BART (Bidirectional and Auto-Regressive Transformers) model, implemented in Python with Hugging ...

Unlike extractive summarization, which involves selecting and rearranging sentences from the source text, abstractive summarization creates new sentences, potentially rephrasing or condensing ...

This paper explores the capabilities of transformer-based models BART, RoBERTa, and PEGASUS in abstractive text summarization. It uses a large diverse dataset of 200,000 rows, which was found to ...

In today’s world, as a substantial amount of textual data is generated, processing and summarizing this data in a meaningful way has become a significant research area. Text summarization aims to ...

Learn about extractive, abstractive, hybrid, and evaluation methods for text summarization in NLP data preprocessing, and how they can help you improve your ML performance.

The research team experimented with Reformer-based models on images and text, using them to generate missing details in images and process the entire novel Crime and Punishment (which contains ...