论文标题
注意差距！为抽象对话摘要注入常识性知识
Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization
论文作者
论文摘要
在本文中，我们建议利用对话的独特特征，共享参与者的常识性知识，以解决总结它们的困难。我们提出了病态的框架，该框架使用常识推论作为其他上下文。与以前仅依赖于输入对话的工作相比，Sick使用外部知识模型来生成一组丰富的常识推论，并选择具有基于相似性选择方法的最可能的推理。基于生病的，生病的++利用常识作为监督，在总结多任务学习环境中的对话时，添加了产生常识推断的任务。实验结果表明，通过注入常识性知识，我们的框架比现有方法产生更多信息和一致的摘要。
In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them. We present SICK, a framework that uses commonsense inferences as additional context. Compared to previous work that solely relies on the input dialogue, SICK uses an external knowledge model to generate a rich set of commonsense inferences and selects the most probable one with a similarity-based selection method. Built upon SICK, SICK++ utilizes commonsense as supervision, where the task of generating commonsense inferences is added upon summarizing the dialogue in a multi-task learning setting. Experimental results show that with injected commonsense knowledge, our framework generates more informative and consistent summaries than existing methods.
