SCURank: Ranking Multiple Candidate Summaries with Summary Content Units for Enhanced Summarization

Researchers propose SCURank, a framework that ranks summary candidates using semantic content units rather than unstable LLM comparisons or surface metrics like ROUGE. The method enables smaller models like BART to match LLM summarization quality through improved distillation from diverse sources.

MentionsSCURank · BART · Summary Content Units · ROUGE

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