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Rongxin Zhu

2 accepted papers

2025

Factual Dialogue Summarization via Learning from Large Language Models

COLING 2025main

Factual consistency is an important quality in dialogue summarization. Large language model (LLM)-based automatic text summarization models generate more factually consistent summaries compared to those by smaller pretrained language models, but they face deployment challenges in real-world applicat…

2023

Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization

ACL 2023long

A series of datasets and models have been proposed for summaries generated for well-formatted documents such as news articles. Dialogue summaries, however, have been under explored. In this paper, we present the first dataset with fine-grained factual error annotations named DIASUMFACT. We define fi…