Semantic Similarity Based Evaluation for Abstractive News Summarization

Figen Beken Fikri, Kemal Oflazer, Berrin Yanikoglu


Abstract
ROUGE is a widely used evaluation metric in text summarization. However, it is not suitable for the evaluation of abstractive summarization systems as it relies on lexical overlap between the gold standard and the generated summaries. This limitation becomes more apparent for agglutinative languages with very large vocabularies and high type/token ratios. In this paper, we present semantic similarity models for Turkish and apply them as evaluation metrics for an abstractive summarization task. To achieve this, we translated the English STSb dataset into Turkish and presented the first semantic textual similarity dataset for Turkish as well. We showed that our best similarity models have better alignment with average human judgments compared to ROUGE in both Pearson and Spearman correlations.
Anthology ID:
2021.gem-1.3
Volume:
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | GEM | IJCNLP
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
24–33
Language:
URL:
https://aclanthology.org/2021.gem-1.3
DOI:
10.18653/v1/2021.gem-1.3
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.gem-1.3.pdf