Abstract
Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to the standard summarization setup which build a single generic summary of a document.We introduce a human-annotated data set EntSUM for controllable summarization with a focus on named entities as the aspects to control.We conduct an extensive quantitative analysis to motivate the task of entity-centric summarization and show that existing methods for controllable summarization fail to generate entity-centric summaries. We propose extensions to state-of-the-art summarization approaches that achieve substantially better results on our data set. Our analysis and results show the challenging nature of this task and of the proposed data set.- Anthology ID:
- 2022.acl-long.237
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3355–3366
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.237
- DOI:
- 10.18653/v1/2022.acl-long.237
- Cite (ACL):
- Mounica Maddela, Mayank Kulkarni, and Daniel Preotiuc-Pietro. 2022. EntSUM: A Data Set for Entity-Centric Extractive Summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3355–3366, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- EntSUM: A Data Set for Entity-Centric Extractive Summarization (Maddela et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.acl-long.237.pdf
- Code
- bloomberg/entsum