Towards Multi-dimensional Evaluation of LLM Summarization across Domains and Languages
This work addresses the need for more comprehensive evaluation frameworks in summarization research, though it is incremental in extending existing benchmarks.
The authors tackled the lack of domain-specific and multilingual evaluation in text summarization by introducing MSumBench, a benchmark that assesses eight models across English and Chinese domains, revealing distinct performance patterns and systematic bias in LLM-based evaluation.
Evaluation frameworks for text summarization have evolved in terms of both domain coverage and metrics. However, existing benchmarks still lack domain-specific assessment criteria, remain predominantly English-centric, and face challenges with human annotation due to the complexity of reasoning. To address these, we introduce MSumBench, which provides a multi-dimensional, multi-domain evaluation of summarization in English and Chinese. It also incorporates specialized assessment criteria for each domain and leverages a multi-agent debate system to enhance annotation quality. By evaluating eight modern summarization models, we discover distinct performance patterns across domains and languages. We further examine large language models as summary evaluators, analyzing the correlation between their evaluation and summarization capabilities, and uncovering systematic bias in their assessment of self-generated summaries. Our benchmark dataset is publicly available at https://github.com/DISL-Lab/MSumBench.