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        <title>Balancing Lexical and Semantic Quality in Abstractive Summarization</title>
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    <abstract>An important problem of the sequence-to-sequence neural models widely used in abstractive summarization is exposure bias. To alleviate this problem, re-ranking systems have been applied in recent years. Despite some performance improvements, this approach remains underexplored. Previous works have mostly specified the rank through the ROUGE score and aligned candidate summaries, but there can be quite a large gap between the lexical overlap metric and semantic similarity. In this paper, we propose a novel training method in which a re-ranker balances the lexical and semantic quality. We further newly define false positives in ranking and present a strategy to reduce their influence. Experiments on the CNN/DailyMail and XSum datasets show that our method can estimate the meaning of summaries without seriously degrading the lexical aspect. More specifically, it achieves an 89.67 BERTScore on the CNN/DailyMail dataset, reaching new state-of-the-art performance. Our code is publicly available at https://github.com/jeewoo1025/BalSum.</abstract>
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%0 Conference Proceedings
%T Balancing Lexical and Semantic Quality in Abstractive Summarization
%A Sul, Jeewoo
%A Choi, Yong Suk
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sul-choi-2023-balancing
%X An important problem of the sequence-to-sequence neural models widely used in abstractive summarization is exposure bias. To alleviate this problem, re-ranking systems have been applied in recent years. Despite some performance improvements, this approach remains underexplored. Previous works have mostly specified the rank through the ROUGE score and aligned candidate summaries, but there can be quite a large gap between the lexical overlap metric and semantic similarity. In this paper, we propose a novel training method in which a re-ranker balances the lexical and semantic quality. We further newly define false positives in ranking and present a strategy to reduce their influence. Experiments on the CNN/DailyMail and XSum datasets show that our method can estimate the meaning of summaries without seriously degrading the lexical aspect. More specifically, it achieves an 89.67 BERTScore on the CNN/DailyMail dataset, reaching new state-of-the-art performance. Our code is publicly available at https://github.com/jeewoo1025/BalSum.
%R 10.18653/v1/2023.acl-short.56
%U https://aclanthology.org/2023.acl-short.56
%U https://doi.org/10.18653/v1/2023.acl-short.56
%P 637-647

Markdown (Informal)

[Balancing Lexical and Semantic Quality in Abstractive Summarization](https://aclanthology.org/2023.acl-short.56) (Sul & Choi, ACL 2023)

• Balancing Lexical and Semantic Quality in Abstractive Summarization (Sul & Choi, ACL 2023)

ACL

• Jeewoo Sul and Yong Suk Choi. 2023. Balancing Lexical and Semantic Quality in Abstractive Summarization. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 637–647, Toronto, Canada. Association for Computational Linguistics.