Abstract
Supervised summarization systems usually rely on supervision at the sentence or n-gram level provided by automatic metrics like ROUGE, which act as noisy proxies for human judgments. In this work, we learn a summary-level scoring function 𝜃 including human judgments as supervision and automatically generated data as regularization. We extract summaries with a genetic algorithm using 𝜃 as a fitness function. We observe strong and promising performances across datasets in both automatic and manual evaluation.- Anthology ID:
- N18-2103
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 654–660
- Language:
- URL:
- https://aclanthology.org/N18-2103
- DOI:
- 10.18653/v1/N18-2103
- Cite (ACL):
- Maxime Peyrard and Iryna Gurevych. 2018. Objective Function Learning to Match Human Judgements for Optimization-Based Summarization. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 654–660, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Objective Function Learning to Match Human Judgements for Optimization-Based Summarization (Peyrard & Gurevych, NAACL 2018)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/N18-2103.pdf