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        <title>Abstractive Document Summarization with Summary-length Prediction</title>
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        <namePart type="family">Kamigaito</namePart>
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    <name type="personal">
        <namePart type="given">Manabu</namePart>
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    <abstract>Recently, we can obtain a practical abstractive document summarization model by fine-tuning a pre-trained language model (PLM). Since the pre-training for PLMs does not consider summarization-specific information such as the target summary length, there is a gap between the pre-training and fine-tuning for PLMs in summarization tasks. To fill the gap, we propose a method for enabling the model to understand the summarization-specific information by predicting the summary length in the encoder and generating a summary of the predicted length in the decoder in fine-tuning. Experimental results on the WikiHow, NYT, and CNN/DM datasets showed that our methods improve ROUGE scores from BART by generating summaries of appropriate lengths. Further, we observed about 3.0, 1,5, and 3.1 point improvements for ROUGE-1, -2, and -L, respectively, from GSum on the WikiHow dataset. Human evaluation results also showed that our methods improve the informativeness and conciseness of summaries.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.45</identifier>
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%0 Conference Proceedings
%T Abstractive Document Summarization with Summary-length Prediction
%A Kwon, Jingun
%A Kamigaito, Hidetaka
%A Okumura, Manabu
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F kwon-etal-2023-abstractive
%X Recently, we can obtain a practical abstractive document summarization model by fine-tuning a pre-trained language model (PLM). Since the pre-training for PLMs does not consider summarization-specific information such as the target summary length, there is a gap between the pre-training and fine-tuning for PLMs in summarization tasks. To fill the gap, we propose a method for enabling the model to understand the summarization-specific information by predicting the summary length in the encoder and generating a summary of the predicted length in the decoder in fine-tuning. Experimental results on the WikiHow, NYT, and CNN/DM datasets showed that our methods improve ROUGE scores from BART by generating summaries of appropriate lengths. Further, we observed about 3.0, 1,5, and 3.1 point improvements for ROUGE-1, -2, and -L, respectively, from GSum on the WikiHow dataset. Human evaluation results also showed that our methods improve the informativeness and conciseness of summaries.
%R 10.18653/v1/2023.findings-eacl.45
%U https://aclanthology.org/2023.findings-eacl.45
%U https://doi.org/10.18653/v1/2023.findings-eacl.45
%P 618-624

Markdown (Informal)

[Abstractive Document Summarization with Summary-length Prediction](https://aclanthology.org/2023.findings-eacl.45) (Kwon et al., Findings 2023)

• Abstractive Document Summarization with Summary-length Prediction (Kwon et al., Findings 2023)

ACL

• Jingun Kwon, Hidetaka Kamigaito, and Manabu Okumura. 2023. Abstractive Document Summarization with Summary-length Prediction. In Findings of the Association for Computational Linguistics: EACL 2023, pages 618–624, Dubrovnik, Croatia. Association for Computational Linguistics.