Evaluating LLMs and Pre-trained Models for Text Summarization Across Diverse Datasets
It provides a benchmark for researchers and practitioners in NLP to compare model performance on text summarization across different datasets, but it is incremental as it applies existing methods to new data.
This study evaluated four pre-trained large language models (BART, FLAN-T5, LLaMA-3-8B, Gemma-7B) on five diverse datasets for text summarization, using metrics like ROUGE and BERTScore to reveal their comparative strengths and limitations.
Text summarization plays a crucial role in natural language processing by condensing large volumes of text into concise and coherent summaries. As digital content continues to grow rapidly and the demand for effective information retrieval increases, text summarization has become a focal point of research in recent years. This study offers a thorough evaluation of four leading pre-trained and open-source large language models: BART, FLAN-T5, LLaMA-3-8B, and Gemma-7B, across five diverse datasets CNN/DM, Gigaword, News Summary, XSum, and BBC News. The evaluation employs widely recognized automatic metrics, including ROUGE-1, ROUGE-2, ROUGE-L, BERTScore, and METEOR, to assess the models' capabilities in generating coherent and informative summaries. The results reveal the comparative strengths and limitations of these models in processing various text types.