Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization. In this work, we explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to multiple speakers, negation understanding, reasoning about the situation, and informal language understanding. Using a pretrained sequence-to-sequence language model, we explore speaker name substitution, negation scope highlighting, multi-task learning with relevant tasks, and pretraining on in-domain data. Our experiments show that our proposed techniques indeed improve summarization performance, outperforming strong baselines.

中文翻译：

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一袋对话总结的技巧

与新闻或科学文章摘要相比，对话摘要有其独特的挑战。在这项工作中，我们探索了该任务的四个不同挑战：处理和区分属于多个说话者的对话部分、否定理解、对情况的推理和非正式语言理解。使用预训练的序列到序列语言模型，我们探索说话人姓名替换、否定范围突出显示、相关任务的多任务学习以及域内数据的预训练。我们的实验表明，我们提出的技术确实提高了摘要性能，优于强基线。