领域经验：一种适用于应用对话摘要的可适应生命周期方法
Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization
AI总结：
本文提出了一种适用于应用对话摘要的可适应生命周期方法，通过行业案例研究探讨了在动态需求下构建可靠摘要系统的挑战与解决方案。
摘要：
多方对话的摘要能力是工业中的关键能力，增强了许多领域中的知识转移和运营效率。然而，自动生成高质量摘要具有挑战性，因为理想的摘要必须满足一组复杂的、多方面的要求。尽管摘要研究受到极大关注，但先前的工作主要使用静态数据集和基准，这在实际场景中很少见，因为要求不可避免地会演变。在本工作中，我们展示了开发一个代理系统来摘要多方互动的行业案例研究。我们分享了贯穿整个开发生命周期的实用见解，以指导从业者构建可靠、适应性强的摘要系统，并为未来研究提供信息，涵盖：1）在要求不断变化的情况下评估的稳健方法，2）由代理架构固有的任务分解所启用的组件级优化，3）上游数据瓶颈的影响，以及4）由于LLM提示的较差可转移性而导致的供应商锁定现实。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
