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.
A comprehensive survey on automatic text summarization with exploration of llm-based methods
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 2years
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A multi-model selection framework that picks the best of three summaries by averaged lexical/semantic scores; the headline performance claims are contradicted by the paper's own tables.
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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.
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A Multi-Model Metric-based Selection Framework for Abstractive Text summarization
A multi-model selection framework that picks the best of three summaries by averaged lexical/semantic scores; the headline performance claims are contradicted by the paper's own tables.