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arXiv · 2407.00747

A Comparative Study of Quality Evaluation Methods for Text Summarization

Huyen Nguyen·Haihua Chen·Lavanya Pobbathi·Junhua Ding
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Abstract

Evaluating text summarization has been a challenging task in natural language processing (NLP). Automatic metrics which heavily rely on reference summaries are not suitable in many situations, while human evaluation is time-consuming and labor-intensive. To bridge this gap, this paper proposes a novel method based on large language models (LLMs) for evaluating text summarization. We also conducts a comparative study on eight automatic metrics, human evaluation, and our proposed LLM-based method. Seven different types of state-of-the-art (SOTA) summarization models were evaluated. We perform extensive experiments and analysis on datasets with patent documents. Our results show that LLMs evaluation aligns closely with human evaluation, while widely-used automatic metrics such as ROUGE-2, BERTScore, and SummaC do not and also lack consistency. Based on the empirical comparison, we propose a LLM-powered framework for automatically evaluating and improving text summarization, which is beneficial and could attract wide attention among the community.

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Huyen Nguyen, Haihua Chen, Lavanya Pobbathi, Junhua Ding. 2024-06-30. A Comparative Study of Quality Evaluation Methods for Text Summarization. https://arxiv.org/abs/2407.00747

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