Research paper

A Large-Scale Multi-Dimensional Empirical Study of LLMs for Conversation Summarization

Published by Association for Computational LinguisticsOct 2026PDF

Compare conversation summarisation systems across several dimensions of quality and performance.

A Large-Scale Multi-Dimensional Empirical Study of LLMs for Conversation Summarization cover

Resource access

Get the full resource

Enter your business details to read the full resource.

First nameLast nameWork emailCompanyJob titleCountry or region

Weixiao Zhou, Gengyao Li, Junnan Zhu, Xianfu Cheng, Feifei Zhai, Zhoujun Li. A Large-Scale Multi-Dimensional Empirical Study of LLMs for Conversation Summarization. Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing. © 2026 Association for Computational Linguistics. CC BY 4.0. 10.18653/2026.emnlp-main.681. Original PDF, unmodified.

CC BY 4.0

A Large-Scale Multi-Dimensional Empirical Study of LLMs for Conversation Summarization

Related research.

ADEPT-SQL: A High-performance Text-to-SQL Application for Real-World Enterprise-Level Databases

Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback

LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL