工业级对话摘要系统开发全周期实战指南
Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization
- 采用智能体架构分解任务，实现模块化优化
- 提出适应动态需求的评估方法，应对主观性挑战
- 揭示数据瓶颈与提示工程不可迁移性的现实问题
多方对话摘要在工业领域至关重要，可提升知识传递与运营效率。然而，自动生成高质量摘要面临挑战，因理想摘要需满足复杂多维要求。现有研究多基于静态数据集与基准，与实际场景中需求持续演化的状况不符。本文通过一个工业案例，展示构建智能体式摘要系统的全过程。分享了覆盖全开发周期的实践经验：1）在需求演变与任务主观性背景下，建立稳健的评估方法；2）利用智能体架构固有的任务分解特性，实现组件级优化；3）揭示上游数据瓶颈对系统性能的制约；4）指出大模型提示工程存在严重不可迁移性，导致供应商锁定的现实困境。
原文摘要 · Abstract (English)
Summarization of multi-party dialogues is a critical capability in industry, enhancing knowledge transfer and operational effectiveness across many domains. However, automatically generating high-quality summaries is challenging, as the ideal summary must satisfy a set of complex, multi-faceted requirements. While summarization has received immense attention in research, prior work has primarily utilized static datasets and benchmarks, a condition rare in practical scenarios where requirements inevitably evolve. In this work, we present an industry case study on developing an agentic system to summarize multi-party interactions. We share practical insights spanning the full development lifecycle to guide practitioners in building reliable, adaptable summarization systems, as well as to inform future research, covering: 1) robust methods for evaluation despite evolving requirements and task subjectivity, 2) component-wise optimization enabled by the task decomposition inherent in an agentic architecture, 3) the impact of upstream data bottlenecks, and 4) the realities of vendor lock-in due to the poor transferability of LLM prompts.
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