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Highlight-Then-Summarize: Learning to Compress Evidence for Long-Context Understanding

Xia, Zhaoyuan et al.
arXiv (All) · Papers
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computation-and-language
Long-context understanding requires large language models (LLMs) to reason over lengthy documents, conversations, and code, yet task-relevant evidence is often sparse and scattered amid substantial irrelevant and redundant content. We propose Highlight-Then-Summarize (H2S), a compress-then-reason pa
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