@inproceedings{wu-etal-2025-incremental,
title = "Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback",
author = "Wu, Yisha and
Zhao, Cen and
Cao, Yuanpei and
Xu, Xiaoqing and
Mehdad, Yashar and
Ji, Mindy and
Cheng, Claire Na",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.140/",
pages = "2000--2015",
ISBN = "979-8-89176-333-3",
abstract = "We introduce an incremental summarization system for customer support agents that intelligently determines when to generate concise bullet notes during conversations, reducing agents' cognitive load and redundant review. Our approach combines a fine-tuned Mixtral-8{\texttimes}7B model for continuous note generation with a DeBERTa-based classifier to filter trivial content. Agent edits refine the online notes generation and regularly inform offline model retraining, closing the agent edits feedback loop. Deployed in production, our system achieved a 3{\%} reduction in case handling time compared to bulk summarization (with reductions of up to 9{\%} in highly complex cases), alongside high agent satisfaction ratings from surveys. These results demonstrate that incremental summarization with continuous feedback effectively enhances summary quality and agent productivity at scale."
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%0 Conference Proceedings
%T Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback
%A Wu, Yisha
%A Zhao, Cen
%A Cao, Yuanpei
%A Xu, Xiaoqing
%A Mehdad, Yashar
%A Ji, Mindy
%A Cheng, Claire Na
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F wu-etal-2025-incremental
%X We introduce an incremental summarization system for customer support agents that intelligently determines when to generate concise bullet notes during conversations, reducing agents’ cognitive load and redundant review. Our approach combines a fine-tuned Mixtral-8×7B model for continuous note generation with a DeBERTa-based classifier to filter trivial content. Agent edits refine the online notes generation and regularly inform offline model retraining, closing the agent edits feedback loop. Deployed in production, our system achieved a 3% reduction in case handling time compared to bulk summarization (with reductions of up to 9% in highly complex cases), alongside high agent satisfaction ratings from surveys. These results demonstrate that incremental summarization with continuous feedback effectively enhances summary quality and agent productivity at scale.
%U https://aclanthology.org/2025.emnlp-industry.140/
%P 2000-2015
Markdown (Informal)
[Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback](https://aclanthology.org/2025.emnlp-industry.140/) (Wu et al., EMNLP 2025)
ACL