AI RESEARCH
LLM-ReSum: A Framework for LLM Reflective Summarization through Self-Evaluation
arXiv CS.CL
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ArXi:2604.25665v1 Announce Type: new Reliable evaluation of large language model (LLM)-generated summaries remains an open challenge, particularly across heterogeneous domains and document lengths. We conduct a comprehensive meta-evaluation of 14 automatic summarization metrics and LLM-based evaluators across seven datasets spanning five domains, covering documents from short news articles to long scientific, governmental, and legal texts (2K-27K words) with over 1,500 human-annotated summaries.