论文标题
利用非拨号摘要进行对话摘要
Leveraging Non-dialogue Summaries for Dialogue Summarization
论文作者
论文摘要
为了减轻学术界缺乏多种对话摘要数据集，我们提出了利用非数字摘要数据来增强对话摘要系统的方法。我们将转换应用于记录摘要数据对，以创建培训数据，以更好地符合对话摘要。建议的转换还保留了非拨号数据集的理想属性，例如提高对源文本的忠诚度。我们在英语和韩语上进行了广泛的实验，以验证我们的方法。尽管引入了更多的对话摘要样本，但利用非拨号数据进行培训，胭脂的绝对收益自然而然地平稳，可显着提高零和少数射击设置的汇总性能，并增强所有培训方案的忠诚。
To mitigate the lack of diverse dialogue summarization datasets in academia, we present methods to utilize non-dialogue summarization data for enhancing dialogue summarization systems. We apply transformations to document summarization data pairs to create training data that better befit dialogue summarization. The suggested transformations also retain desirable properties of non-dialogue datasets, such as improved faithfulness to the source text. We conduct extensive experiments across both English and Korean to verify our approach. Although absolute gains in ROUGE naturally plateau as more dialogue summarization samples are introduced, utilizing non-dialogue data for training significantly improves summarization performance in zero- and few-shot settings and enhances faithfulness across all training regimes.
