Abstract
Dialogue summarization aims to condense a given dialogue into a simple and focused summary text. Typically, both the roles' viewpoints and conversational topics change in the dialogue stream. Thus how to effectively handle the shifting topics and select the most salient utterance becomes one of the major challenges of this task. In this paper, we propose a novel topic-aware Global-Local Centrality (GLC) model to help select the salient context from all sub-topics. The centralities are constructed at both the global and local levels. The global one aims to identify vital sub-topics in the dialogue and the local one aims to select the most important context in each sub-topic. Specifically, the GLC collects sub-topic based on the utterance representations. And each utterance is aligned with one sub-topic. Based on the sub-topics, the GLC calculates global- and local-level centralities. Finally, we combine the two to guide the model to capture both salient context and sub-topics when generating summaries. Experimental results show that our model outperforms strong baselines on three public dialogue summarization datasets: CSDS, MC, and SAMSUM. Further analysis demonstrates that our GLC can exactly identify vital contents from sub-topics.~\footnote{\url{this https URL}}
Abstract (translated)
对话摘要旨在将给定的对话压缩成简单且重点突出的摘要文本。通常情况下,对话角色的观点和对话话题在对话流中会发生变化。因此,如何有效地处理变化的话题和选择最引人注目的言简意赅的说话内容成为这个任务的主要挑战之一。在本文中,我们提出了一种新的话题Aware Global-Local Centrality (GLC)模型,以帮助选择所有子话题中的引人注目上下文。上下文是在 global 和 local 层面上构建的。global 上下文旨在识别对话中的 vital 子话题,而 local 上下文旨在选择每个子话题中最重要的上下文。具体来说,GLC 通过说话内容表示收集子话题。每个说话内容都与一个子话题对齐。基于子话题,GLC 计算 global 和 local 层面的中心性。最后,我们结合了两个模型,以指导模型在生成摘要时捕获引人注目的上下文和子话题。实验结果显示,我们在三个公共对话摘要数据集上:CSDS、MC 和 SAMSUM中的表现优于强基准模型。进一步分析表明,我们的 GLMC 可以准确地从子话题中识别关键内容。
URL
https://arxiv.org/abs/2301.12376
