An Empirical Comparison of Text Summarization: A Multi-Dimensional Evaluation of Large Language Models
Anantharaman Janakiraman, Behnaz Ghoraani·April 06, 2025
Summary
A study evaluated 17 large language models for text summarization, focusing on metrics like factual consistency, semantic similarity, and human-like quality. Models varied in performance, with some excelling in specific areas. The research offers recommendations for different use cases, emphasizing the importance of balancing factors for effective summarization systems. It introduces a multidimensional evaluation framework, featuring models like deepseek-v3, claude-3-5-sonnet, gpt-3.5-turbo, and gemini-1.5-flash for various applications. The study includes dataset-specific performance visualizations, highlighting models' effectiveness across metrics.
Introduction
Background
Overview of text summarization and its importance
Brief history and evolution of large language models
Objective
To evaluate 17 large language models for text summarization based on metrics like factual consistency, semantic similarity, and human-like quality
Method
Data Collection
Description of the dataset used for evaluation
Criteria for selecting the models for evaluation
Data Preprocessing
Techniques used for preparing the data for evaluation
Handling of missing or incomplete data
Evaluation Metrics
Detailed explanation of the metrics used (factual consistency, semantic similarity, human-like quality)
Weighting and balancing of these metrics in the evaluation framework
Results
Model Performance
Overview of the models' performance across the evaluation metrics
Identification of models excelling in specific areas
Model Comparison
Comparative analysis of the models based on their performance
Discussion on the trade-offs between different models
Discussion
Recommendations
Based on the evaluation, recommendations for different use cases
Importance of balancing factors for effective summarization systems
Multidimensional Evaluation Framework
Introduction of a framework that considers multiple dimensions of model performance
Explanation of the framework's components (e.g., deepseek-v3, claude-3-5-sonnet, gpt-3.5-turbo, gemini-1.5-flash)
Dataset-Specific Performance Visualizations
Visual representation of models' effectiveness across different metrics
Insights into models' performance in specific contexts or domains
Conclusion
Summary of Findings
Recap of the key insights from the evaluation
Future Directions
Potential areas for further research
Implications for the development of summarization systems
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
How does the multidimensional evaluation framework assess the performance of models like deepseek-v3 and gpt-3.5-turbo?
What are the main objectives and findings of the study evaluating large language models for text summarization?
What innovative approaches does the study introduce for evaluating text summarization models?
What limitations are identified in the study regarding the evaluation of large language models for summarization?