@inproceedings{xu-etal-2022-narrate,
    title = "Narrate Dialogues for Better Summarization",
    author = "Xu, Ruochen  and
      Zhu, Chenguang  and
      Zeng, Michael",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.findings-emnlp.261/",
    doi = "10.18653/v1/2022.findings-emnlp.261",
    pages = "3565--3575",
    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."
}