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        <title>Narrate Dialogues for Better Summarization</title>
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        <namePart type="given">Ruochen</namePart>
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        <namePart type="given">Chenguang</namePart>
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            <namePart type="given">Yue</namePart>
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    <abstract>Dialogue summarization models aim to generate a concise and accurate summary for multi-party dialogue. The complexity of dialogue, including coreference, dialogue acts, and inter-speaker interactions bring unique challenges to dialogue summarization. Most recent neural models achieve state-of-art performance following the pretrain-then-finetune recipe, where the large-scale language model (LLM) is pretrained on large-scale single-speaker written text, but later finetuned on multi-speaker dialogue text. To mitigate the gap between pretraining and finetuning, we propose several approaches to convert the dialogue into a third-person narrative style and show that the narration serves as a valuable annotation for LLMs. Empirical results on three benchmark datasets show our simple approach achieves higher scores on the ROUGE and a factual correctness metric.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.261</identifier>
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%0 Conference Proceedings
%T Narrate Dialogues for Better Summarization
%A Xu, Ruochen
%A Zhu, Chenguang
%A Zeng, Michael
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F xu-etal-2022-narrate
%X Dialogue summarization models aim to generate a concise and accurate summary for multi-party dialogue. The complexity of dialogue, including coreference, dialogue acts, and inter-speaker interactions bring unique challenges to dialogue summarization. Most recent neural models achieve state-of-art performance following the pretrain-then-finetune recipe, where the large-scale language model (LLM) is pretrained on large-scale single-speaker written text, but later finetuned on multi-speaker dialogue text. To mitigate the gap between pretraining and finetuning, we propose several approaches to convert the dialogue into a third-person narrative style and show that the narration serves as a valuable annotation for LLMs. Empirical results on three benchmark datasets show our simple approach achieves higher scores on the ROUGE and a factual correctness metric.
%R 10.18653/v1/2022.findings-emnlp.261
%U https://aclanthology.org/2022.findings-emnlp.261
%U https://doi.org/10.18653/v1/2022.findings-emnlp.261
%P 3565-3575

Markdown (Informal)

[Narrate Dialogues for Better Summarization](https://aclanthology.org/2022.findings-emnlp.261) (Xu et al., Findings 2022)

• Narrate Dialogues for Better Summarization (Xu et al., Findings 2022)

ACL

• Ruochen Xu, Chenguang Zhu, and Michael Zeng. 2022. Narrate Dialogues for Better Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3565–3575, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.