Transformer Based Summarization Model

Transformer-based models are revolutionizing text summarization by generating abstractive summaries that go beyond simple extraction. Current research focuses on improving these models' accuracy, particularly in addressing factual errors and handling long documents, often through techniques like ensemble methods, attention head manipulation, and curriculum learning. These advancements are crucial for various applications, including automatic minuting, literature review generation, and efficient information retrieval across diverse domains, ultimately enhancing human productivity and knowledge synthesis.