Abstract
Keywords, that is, content-relevant words in summaries play an important role in efficient information conveyance, making it critical to assess if system-generated summaries contain such informative words during evaluation. However, existing evaluation metrics for extreme summarization models do not pay explicit attention to keywords in summaries, leaving developers ignorant of their presence. To address this issue, we present a keyword-oriented evaluation metric, dubbed ROUGE-K, which provides a quantitative answer to the question of – How well do summaries include keywords? Through the lens of this keyword-aware metric, we surprisingly find that a current strong baseline model often misses essential information in their summaries. Our analysis reveals that human annotators indeed find the summaries with more keywords to be more relevant to the source documents. This is an important yet previously overlooked aspect in evaluating summarization systems. Finally, to enhance keyword inclusion, we propose four approaches for incorporating word importance into a transformer-based model and experimentally show that it enables guiding models to include more keywords while keeping the overall quality.- Anthology ID:
- 2024.starsem-1.6
- Volume:
- Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Danushka Bollegala, Vered Shwartz
- Venue:
- *SEM
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 69–79
- Language:
- URL:
- https://aclanthology.org/2024.starsem-1.6
- DOI:
- Cite (ACL):
- Sotaro Takeshita, Simone Ponzetto, and Kai Eckert. 2024. ROUGE-K: Do Your Summaries Have Keywords?. In Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024), pages 69–79, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- ROUGE-K: Do Your Summaries Have Keywords? (Takeshita et al., *SEM 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.starsem-1.6.pdf