解决对话摘要中关键信息缺失问题，让系统自动补全上下文证据。
Don't Scroll Back: Missing-Evidence Memory for Streaming Dialogue Summarization
- 基于未解决的上下文依赖动态检索历史信息
- 在16万词长历史下提升记忆召回率与缺口填补率
- 适合需要实时摘要的对话系统开发者
现代平台用户频繁需要近期对话的摘要，但当前窗口常缺乏足够上下文独立理解。本文将此场景定义为流式对话摘要任务：系统需在固定预算内，从无限历史中选择性记忆以总结当前窗口。核心挑战不在于访问多少历史，而在于记忆是否包含当前窗口所依赖的关键证据。为此构建基准与评估协议，分别检验记忆是否包含填补空白的证据，以及摘要是否反映这些证据。提出ReMEMBER框架，通过未解依赖条件化检索，并在固定预算下提炼出高密度证据记忆。在长达16万词的历史对话上实验表明，相较于基线方法，ReMEMBER显著提升记忆召回率与缺口填补完整性。
原文摘要 · Abstract (English)
Users of modern platforms repeatedly need summaries of recent dialogue, but the window rarely contains enough context to be interpreted on its own. We formalize this setting as streaming dialogue summarization, where a system must summarize a current window using selective memory from an unbounded history under a fixed budget. We show that the central challenge is not how much history is accessed, but whether memory recovers the evidence that the current window presupposes. We construct a benchmark and evaluation protocol that separately assesses whether memory contains gap-resolving evidence and whether the generated summary reflects it. We propose ReMEMBER, a missing-evidence memory framework that conditions retrieval on unresolved window dependencies and refines retrieved chunks into evidence-dense memory under a fixed budget. Experiments on dialogues with histories up to 160K tokens show that ReMEMBER improves memory recall and gap-resolution completeness over memory construction baselines under the same budget.
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