Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning

arXiv:2405.02710v1 Announce Type: new
Abstract: With the deluge of information delivered by the daily news cycle, there is a growing need to effectively and efficiently summarize news feeds for quick consumption. We leverage large language models (LLMs), with their advanced learning and generative abilities as compared to conventional language models, to generate concise and coherent summaries for news articles from the XSum dataset. Our paper focuses on two key aspects of LLMs: Efficient in-context Learning (ELearn) and Parameter Efficient Fine-tuning …

abstract advanced arxiv consumption context context learning cs.cl daily fine-tuning generative in-context learning information language language models large language large language models llms summarization through type