LLM-ReSum uses LLM self-evaluation in a closed feedback loop to refine summaries, improving factual accuracy by up to 33% and coverage by 39% with 89% human preference.
Quality evaluation of summarization models for patent documents,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
LongSumEval evaluates long-document summaries via answerability and factual alignment of generated QA pairs, yielding stronger human correlation than prior metrics and enabling iterative self-improvement.
citing papers explorer
-
LLM-ReSum: A Framework for LLM Reflective Summarization through Self-Evaluation
LLM-ReSum uses LLM self-evaluation in a closed feedback loop to refine summaries, improving factual accuracy by up to 33% and coverage by 39% with 89% human preference.
-
LongSumEval: Question-Answering Based Evaluation and Feedback-Driven Refinement for Long Document Summarization
LongSumEval evaluates long-document summaries via answerability and factual alignment of generated QA pairs, yielding stronger human correlation than prior metrics and enabling iterative self-improvement.