Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization
This work addresses the problem of building adaptable dialogue summarization systems for industry practitioners, but it is incremental as it focuses on practical guidance rather than introducing new methods.
The paper tackles the challenge of automatically generating high-quality summaries for multi-party dialogues in industry, where requirements evolve, by presenting a case study on developing an agentic system and sharing practical insights across the development lifecycle.
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.