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.
CITATION STYLE
Xiao, W., & Carenini, G. (2023). Entity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 70–81). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.codi-1.9
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