NAACL 2022long14 citations
Reference-free Summarization Evaluation via Semantic Correlation and Compression Ratio
Abstract
A document can be summarized in a number of ways. Reference-based evaluation of summarization has been criticized for its inflexibility. The more sufficient the number of abstracts, the more accurate the evaluation results. However, it is difficult to collect sufficient reference summaries. In this paper, we propose a new automatic reference-free evaluation metric that compares semantic distribution between source document and summary by pretrained language models and considers summary compression ratio. The experiments show that this metric is more consistent with human evaluation in terms of coherence, consistency, relevance and fluency.
BibTeX
@inproceedings{liu-etal-2022-reference,
title = "Reference-free Summarization Evaluation via Semantic Correlation and Compression Ratio",
author = "Liu, Yizhu and
Jia, Qi and
Zhu, Kenny",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.153/",
doi = "10.18653/v1/2022.naacl-main.153",
pages = "2109--2115"
}