A Comparative Study of Quality Evaluation Methods for Text Summarization
This addresses the problem of unreliable automatic evaluation for text summarization in NLP, offering a more efficient alternative to human evaluation, though it is incremental as it builds on existing LLM capabilities.
The paper tackles the challenge of evaluating text summarization by proposing a novel method based on large language models (LLMs) and comparing it with eight automatic metrics and human evaluation, showing that LLM evaluation aligns closely with human evaluation while metrics like ROUGE-2, BERTScore, and SummaC do not.
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.