  Meeting summarization has become a critical task since digital encounters
have become a common practice. Large language models (LLMs) show great
potential in summarization, offering enhanced coherence and context
understanding compared to traditional methods. However, they still struggle to
maintain relevance and avoid hallucination. We introduce a multi-LLM correction
approach for meeting summarization using a two-phase process that mimics the
human review process: mistake identification and summary refinement. We release
QMSum Mistake, a dataset of 200 automatically generated meeting summaries
annotated by humans on nine error types, including structural, omission, and
irrelevance errors. Our experiments show that these errors can be identified
with high accuracy by an LLM. We transform identified mistakes into actionable
feedback to improve the quality of a given summary measured by relevance,
informativeness, conciseness, and coherence. This post-hoc refinement
effectively improves summary quality by leveraging multiple LLMs to validate
output quality. Our multi-LLM approach for meeting summarization shows
potential for similar complex text generation tasks requiring robustness,
action planning, and discussion towards a goal.
