「不要回翻：流式對話摘要的缺失證據記憶」

摘要

現代平台的使用者反覆需要近期對話的摘要，但視窗中通常沒有足夠的上下文可以獨立解讀。我們將此情境形式化為串流對話摘要，即系統必須在固定預算下，利用無界歷史中的選擇性記憶來總結當前視窗。我們證明，核心挑戰不在於存取了多少歷史，而在於記憶是否恢復了當前視窗所預設的證據。我們建構了一個基準與評測協議，分別評估記憶是否包含解決缺口的證據，以及生成的摘要是否反映該證據。我們提出 ReMEMBER，一個缺失證據記憶框架，其根據未解決的視窗依賴關係來條件化檢索，並在固定預算下將檢索到的片段精煉為證據密集的記憶。在歷史長達 160K tokens 的對話實驗中，ReMEMBER 在相同預算下，相較於記憶建構基線，改善了記憶召回率與缺口解析完整性。

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.