Dialogue Summarization with Static-Dynamic Structure Fusion Graph (2023)

Abstract: Dialogue, the most fundamental and specially privileged arena of language, gains increasing ubiquity across the Web in recent years. Quickly going through the long dialogue context and capturing salient information scattered over the whole dialogue session benefit users in many real-world Web applications such as email thread summarization and meeting minutes draft. Dialogue summarization is a challenging task in that dialogue has dynamic interaction nature and presumably inconsistent information flow among various speakers. Many researchers address this task by modeling dialogue with pre-computed static graph structure using external linguistic toolkits. However, such methods heavily depend on the reliability of external tools and the static graph construction is disjoint with the graph representation learning phase, which makes the graph can’t be dynamically adapted for the downstream summarization task. In this paper, we propose a Static-Dynamic graph-based Dialogue Summarization model (SDDS), which fuses prior knowledge from human expertise and adaptively learns the graph structure in an end-to-end learning fashion. To verify the effectiveness of SDDS, we conduct experiments on three benchmark datasets (SAMSum, MediaSum, and DialogSum) and the results verify the superiority of SDDS.

## introduction dialogue summarization, aiming at distilling the salient information from a dialogue context into a concise summary, is one of the most challenging and intriguing tasks in text summarization @xcite @xcite . it can help people quickly capture the highlights of a semi-structured and multi-participant dialogue without reviewing the complex dialogue context @xcite input: dialogue context output: generated summary a: i got my concert ticket b: whooaa that's great! c: i will see you there a: yes for sure. c: who you're going with? i will ask d a got a ticket for concert. c and b are going, too. a got a ticket for concert. c is going, too. ## sdds model in this section, we introduce the static-dynamic graph based dialogue summarization model (sdds). an overview is shown in figure 2 . ## conclusion in this paper, we first investigate the limitation of the current static graph-based dialogue summarization methods and propose a static-dynamic graphbased dialogue summarization method (sdds). it contains two modules, a static graph module and a dynamic graph module. the former injects human prior into the summarization model and the latter encodes the implicit knowledge from a pretrained language model. by fusing these two kinds of graphs with a fine-grained 1×1 convolution, sdds could adaptively adjust the graph weight and learn the graph structure in an end-to-end learning fashion from the supervision of the summarization task. to validate the effectiveness of sdds, we conduct extensive experiments on three public dialogue summarization datasets (samsum, me-diasum, and dialogsum) and observe significant improvement over strong baselines. we also carefully examine each key component and gives a detailed analysis of sdds for future research.