The purpose of developing an abstractive text summarizer is to automatically generate concise and coherent summaries of longer texts while preserving the main ideas and key information. Abstractive summarization is a task typically associated with natural language processing (NLP).
• BART-Large is a variant of the BART (Bidirectional and Auto-Regressive Transformers) model, which is a powerful language generation model. BART-Large is specifically trained on a large-scale dataset and has a larger model size compared to the base BART model.
• With increased capacity and parameters, BART-Large demonstrates enhanced performance in various natural language processing tasks such as text summarization, translation, and text generation. The larger size of BART-Large allows it to capture more intricate language patterns, leading to improved language understanding and generation capabilities.
