@inproceedings{hofmann-coyle-etal-2022-extractive,
title = "Extractive Entity-Centric Summarization as Sentence Selection using Bi-Encoders",
author = "Hofmann-Coyle, Ella and
Kulkarni, Mayank and
Xie, Lingjue and
Maddela, Mounica and
Preotiuc-Pietro, Daniel",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-short.40",
pages = "326--333",
abstract = "Entity-centric summarization is a type of controllable summarization that aims to produce a summary of a document that is specific to a given target entity. Extractive summaries possess multiple advantages over abstractive ones such as preserving factuality and can be directly used in downstream tasks like target-based sentiment analysis or incorporated into search applications. In this paper, we explore methods to solve this task by recasting it as a sentence selection task, as supported by the EntSUM data set. We use methods inspired by information retrieval, where the input to the model is a pair representing a sentence from the original document and the target entity, in place of the query. We explore different architecture variants and loss functions in this framework with results showing an up to 5.8 F1 improvement over past state-of-the-art and outperforming the competitive entity-centric Lead 3 heuristic by 1.1 F1. In addition, we also demonstrate similarly strong results on the related task of salient sentence selection for an entity.",
}
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<abstract>Entity-centric summarization is a type of controllable summarization that aims to produce a summary of a document that is specific to a given target entity. Extractive summaries possess multiple advantages over abstractive ones such as preserving factuality and can be directly used in downstream tasks like target-based sentiment analysis or incorporated into search applications. In this paper, we explore methods to solve this task by recasting it as a sentence selection task, as supported by the EntSUM data set. We use methods inspired by information retrieval, where the input to the model is a pair representing a sentence from the original document and the target entity, in place of the query. We explore different architecture variants and loss functions in this framework with results showing an up to 5.8 F1 improvement over past state-of-the-art and outperforming the competitive entity-centric Lead 3 heuristic by 1.1 F1. In addition, we also demonstrate similarly strong results on the related task of salient sentence selection for an entity.</abstract>
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%0 Conference Proceedings
%T Extractive Entity-Centric Summarization as Sentence Selection using Bi-Encoders
%A Hofmann-Coyle, Ella
%A Kulkarni, Mayank
%A Xie, Lingjue
%A Maddela, Mounica
%A Preotiuc-Pietro, Daniel
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F hofmann-coyle-etal-2022-extractive
%X Entity-centric summarization is a type of controllable summarization that aims to produce a summary of a document that is specific to a given target entity. Extractive summaries possess multiple advantages over abstractive ones such as preserving factuality and can be directly used in downstream tasks like target-based sentiment analysis or incorporated into search applications. In this paper, we explore methods to solve this task by recasting it as a sentence selection task, as supported by the EntSUM data set. We use methods inspired by information retrieval, where the input to the model is a pair representing a sentence from the original document and the target entity, in place of the query. We explore different architecture variants and loss functions in this framework with results showing an up to 5.8 F1 improvement over past state-of-the-art and outperforming the competitive entity-centric Lead 3 heuristic by 1.1 F1. In addition, we also demonstrate similarly strong results on the related task of salient sentence selection for an entity.
%U https://aclanthology.org/2022.aacl-short.40
%P 326-333
Markdown (Informal)
[Extractive Entity-Centric Summarization as Sentence Selection using Bi-Encoders](https://aclanthology.org/2022.aacl-short.40) (Hofmann-Coyle et al., AACL-IJCNLP 2022)
ACL
- Ella Hofmann-Coyle, Mayank Kulkarni, Lingjue Xie, Mounica Maddela, and Daniel Preotiuc-Pietro. 2022. Extractive Entity-Centric Summarization as Sentence Selection using Bi-Encoders. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 326–333, Online only. Association for Computational Linguistics.