@inproceedings{maddela-etal-2022-entsum,
title = "{E}nt{SUM}: A Data Set for Entity-Centric Extractive Summarization",
author = "Maddela, Mounica and
Kulkarni, Mayank and
Preotiuc-Pietro, Daniel",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.237",
doi = "10.18653/v1/2022.acl-long.237",
pages = "3355--3366",
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.",
}
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%0 Conference Proceedings
%T EntSUM: A Data Set for Entity-Centric Extractive Summarization
%A Maddela, Mounica
%A Kulkarni, Mayank
%A Preotiuc-Pietro, Daniel
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F maddela-etal-2022-entsum
%X 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.
%R 10.18653/v1/2022.acl-long.237
%U https://aclanthology.org/2022.acl-long.237
%U https://doi.org/10.18653/v1/2022.acl-long.237
%P 3355-3366
Markdown (Informal)
[EntSUM: A Data Set for Entity-Centric Extractive Summarization](https://aclanthology.org/2022.acl-long.237) (Maddela et al., ACL 2022)
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.