Factual consistency evaluation of summarization in the Era of large language models-Reference-Cited by-同舟云学术

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Factual consistency evaluation of summarization in the Era of large language models

Published:2024-11 Issue: Volume:254 Page:124456
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Luo Zheheng^ORCID,Xie Qianqian,Ananiadou Sophia

Publisher

Elsevier BV

Reference50 articles.

1. The balanced accuracy and its posterior distribution;Brodersen,2010

2. FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization;Durmus,2020

3. FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization, arxiv, abs/2005.03754;Durmus,2020

4. Summeval: Re-evaluating summarization evaluation;Fabbri;Transactions of the Association for Computational Linguistics,2021

5. QaFactEval: Improved QA-based factual consistency evaluation for summarization;Fabbri,2021

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