An Empirical Comparison of Text Summarization: A Multi-Dimensional Evaluation of Large Language Models

• URL: http://arxiv.org/abs/2504.04534v1
• Date: Sun, 06 Apr 2025 16:24:22 GMT
• Title: An Empirical Comparison of Text Summarization: A Multi-Dimensional Evaluation of Large Language Models
• Authors: Anantharaman Janakiraman, Behnaz Ghoraani,
• Abstract summary: This research evaluates summarization performance across 17 large language models (OpenAI, Google, Anthropic, open-source)<n>We assessed models on seven diverse datasets using metrics for factual consistency, semantic similarity, lexical overlap, and human-like quality.
• Score: 2.1945750784330067
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text summarization is crucial for mitigating information overload across domains like journalism, medicine, and business. This research evaluates summarization performance across 17 large language models (OpenAI, Google, Anthropic, open-source) using a novel multi-dimensional framework. We assessed models on seven diverse datasets (BigPatent, BillSum, CNN/DailyMail, PubMed, SAMSum, WikiHow, XSum) at three output lengths (50, 100, 150 tokens) using metrics for factual consistency, semantic similarity, lexical overlap, and human-like quality, while also considering efficiency factors. Our findings reveal significant performance differences, with specific models excelling in factual accuracy (deepseek-v3), human-like quality (claude-3-5-sonnet), and processing efficiency/cost-effectiveness (gemini-1.5-flash, gemini-2.0-flash). Performance varies dramatically by dataset, with models struggling on technical domains but performing well on conversational content. We identified a critical tension between factual consistency (best at 50 tokens) and perceived quality (best at 150 tokens). Our analysis provides evidence-based recommendations for different use cases, from high-stakes applications requiring factual accuracy to resource-constrained environments needing efficient processing. This comprehensive approach enhances evaluation methodology by integrating quality metrics with operational considerations, incorporating trade-offs between accuracy, efficiency, and cost-effectiveness to guide model selection for specific applications.

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