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Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization

Kushal Chawla, Chenyang Zhu, Pengshan Cai, Sangwoo Cho, Scott Novotney, Ayushman Singh, Jonah Lewis, Keasha Safewright, Alfy Samuel, Erin Babinsky, Shi-Xiong Zhang, Sambit Sahu

TL;DR

The paper analyzes an industry-driven, multi-party dialogue summarization problem where evolving user requirements complicate evaluation and optimization. It advocates an adaptable, agentic lifecycle that decomposes tasks into drafting and iterative, component-wise refinement, supported by a hybrid evaluation protocol (human judgment plus an LLM-based AutoEval). Key findings show that component-wise tuning yields marked gains over end-to-end approaches and that upstream ASR noise and prompt portability across LLMs pose significant practical challenges. The study demonstrates practical pathways to robust, adaptable summarization in real-world settings and highlights open issues in latency, data quality, and model interoperability with implications for deployment in industry contexts.

Abstract

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.

Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization

TL;DR

The paper analyzes an industry-driven, multi-party dialogue summarization problem where evolving user requirements complicate evaluation and optimization. It advocates an adaptable, agentic lifecycle that decomposes tasks into drafting and iterative, component-wise refinement, supported by a hybrid evaluation protocol (human judgment plus an LLM-based AutoEval). Key findings show that component-wise tuning yields marked gains over end-to-end approaches and that upstream ASR noise and prompt portability across LLMs pose significant practical challenges. The study demonstrates practical pathways to robust, adaptable summarization in real-world settings and highlights open issues in latency, data quality, and model interoperability with implications for deployment in industry contexts.

Abstract

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.
Paper Structure (12 sections, 3 figures, 6 tables)

This paper contains 12 sections, 3 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Use Case Description
  3. Evaluation Protocols
  4. System Design and Performance
  5. A Decomposed Agentic Architecture
  6. Performance Analysis
  7. Discussion
  8. The Upstream Data Bottleneck
  9. Prompt Portability
  10. Conclusion
  11. Examples
  12. Evolving Requirements

Figures (3)

  • Figure 1: Overview of our summarization system. Left: End-to-end workflow. Right: Our agentic architecture, including the drafting of an initial summary along with iterative evaluation and refinement.
  • Figure 2: Results from multiple rounds of human preference A/B tests comparing candidate models directly with gold summaries. Monolithic refers to the baseline single LLM system. Agentic v1 to v5 represents variants of our agentic approach presented in Figure \ref{['fig:agentic-architecture']}.
  • Figure 3: Throughout model development, it's crucial to incorporate feedback from annotators and stakeholders by updating evaluation guidelines. While this adaptability requires adjustments to evaluation, it ultimately ensures the developed system remains aligned with user requirements.