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

arXiv: https://arxiv.org/abs/2601.08682 · HTML: https://mlsanity.com/paper/2601.08682/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [MeetingBank: A Benchmark Dataset for Meeting Summarization](https://mlsanity.com/paper/2305.17529/) (2023)
- [ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts](https://mlsanity.com/paper/2210.12467/) (2022)
- [AutoPDL: Automatic Prompt Optimization for LLM Agents](https://mlsanity.com/paper/2504.04365/) (2025)
- [NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization](https://mlsanity.com/paper/2505.24575/) (2025)
- [Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarization](https://mlsanity.com/paper/2410.14545/) (2024)
- [An End-to-End Dialogue Summarization System for Sales Calls](https://mlsanity.com/paper/2204.12951/) (2022)
- [Learning to Summarize by Learning to Quiz: Adversarial Agentic Collaboration for Long Document Summarization](https://mlsanity.com/paper/2509.20900/) (2025)

## Built on by (most cited first, 1 of 1)

- [Position: Natural Language Should Not Fully Replace Formal Languages](https://mlsanity.com/paper/2607.20432/) (2026)

## Abstract (arXiv)

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.