@inproceedings{xiao-carenini-2023-entity,
title = "Entity-based {S}pan{C}opy for Abstractive Summarization to Improve the Factual Consistency",
author = "Xiao, Wen and
Carenini, Giuseppe",
editor = "Strube, Michael and
Braud, Chloe and
Hardmeier, Christian and
Li, Junyi Jessy and
Loaiciga, Sharid and
Zeldes, Amir",
booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.codi-1.9",
doi = "10.18653/v1/2023.codi-1.9",
pages = "70--81",
abstract = "Discourse-aware techniques, including entity-aware approaches, play a crucial role in summarization. In this paper, we propose an entity-based SpanCopy mechanism to tackle the entity-level factual inconsistency problem in abstractive summarization, i.e. reducing the mismatched entities between the generated summaries and the source documents. Complemented by a Global Relevance component to identify summary-worthy entities, our approach demonstrates improved factual consistency while preserving saliency on four summarization datasets, contributing to the effective application of discourse-aware methods summarization tasks.",
}
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<abstract>Discourse-aware techniques, including entity-aware approaches, play a crucial role in summarization. In this paper, we propose an entity-based SpanCopy mechanism to tackle the entity-level factual inconsistency problem in abstractive summarization, i.e. reducing the mismatched entities between the generated summaries and the source documents. Complemented by a Global Relevance component to identify summary-worthy entities, our approach demonstrates improved factual consistency while preserving saliency on four summarization datasets, contributing to the effective application of discourse-aware methods summarization tasks.</abstract>
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%0 Conference Proceedings
%T Entity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency
%A Xiao, Wen
%A Carenini, Giuseppe
%Y Strube, Michael
%Y Braud, Chloe
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Loaiciga, Sharid
%Y Zeldes, Amir
%S Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F xiao-carenini-2023-entity
%X Discourse-aware techniques, including entity-aware approaches, play a crucial role in summarization. In this paper, we propose an entity-based SpanCopy mechanism to tackle the entity-level factual inconsistency problem in abstractive summarization, i.e. reducing the mismatched entities between the generated summaries and the source documents. Complemented by a Global Relevance component to identify summary-worthy entities, our approach demonstrates improved factual consistency while preserving saliency on four summarization datasets, contributing to the effective application of discourse-aware methods summarization tasks.
%R 10.18653/v1/2023.codi-1.9
%U https://aclanthology.org/2023.codi-1.9
%U https://doi.org/10.18653/v1/2023.codi-1.9
%P 70-81
Markdown (Informal)
[Entity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency](https://aclanthology.org/2023.codi-1.9) (Xiao & Carenini, CODI 2023)
ACL