@inproceedings{chawla-etal-2026-lessons,
title = "Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization",
author = "Chawla, Kushal and
Zhu, Chenyang and
Cai, Pengshan and
Cho, Sangwoo and
Novotney, Scott and
Singh, Ayushman and
Lewis, Jonah and
Safewright, Keasha and
Samuel, Alfy and
Babinsky, Erin and
Zhang, Shi-Xiong and
Sahu, Sambit",
editor = {Matusevych, Yevgen and
Eryi{\u{g}}it, G{\"u}l{\c{s}}en and
Aletras, Nikolaos},
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-industry.41/",
pages = "535--544",
ISBN = "979-8-89176-384-5",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization
%A Chawla, Kushal
%A Zhu, Chenyang
%A Cai, Pengshan
%A Cho, Sangwoo
%A Novotney, Scott
%A Singh, Ayushman
%A Lewis, Jonah
%A Safewright, Keasha
%A Samuel, Alfy
%A Babinsky, Erin
%A Zhang, Shi-Xiong
%A Sahu, Sambit
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F chawla-etal-2026-lessons
%X 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.
%U https://aclanthology.org/2026.eacl-industry.41/
%P 535-544
Markdown (Informal)
[Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization](https://aclanthology.org/2026.eacl-industry.41/) (Chawla et al., EACL 2026)
ACL
- Kushal Chawla, Chenyang Zhu, Pengshan Cai, Sangwoo Cho, Scott Novotney, Ayushman Singh, Jonah Lewis, Keasha Safewright, Alfy Samuel, Erin Babinsky, Shi-Xiong Zhang, and Sambit Sahu. 2026. Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track), pages 535–544, Rabat, Morocco. Association for Computational Linguistics.