Evaluating LLMs and Pre-trained Models for Text Summarization Across Diverse Datasets

• URL: http://arxiv.org/abs/2502.19339v2
• Date: Thu, 13 Mar 2025 09:40:42 GMT
• Title: Evaluating LLMs and Pre-trained Models for Text Summarization Across Diverse Datasets
• Authors: Tohida Rehman, Soumabha Ghosh, Kuntal Das, Souvik Bhattacharjee, Debarshi Kumar Sanyal, Samiran Chattopadhyay,
• Abstract summary: This study offers a thorough evaluation of four leading large language models: BART, FLAN-T5, LLaMA-3-8B, and Gemma-7B.<n>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.
• Score: 2.6966823536477436
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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