Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions (2022)

Abstract: Role-oriented dialogue summarization is to generate summaries for different roles in the dialogue, e.g., merchants and consumers. Existing methods handle this task by summarizing each role’s content separately and thus are prone to ignore the information from other roles. However, we believe that other roles’ content could benefit the quality of summaries, such as the omitted information mentioned by other roles. Therefore, we propose a novel role interaction enhanced method for role-oriented dialogue summarization. It adopts cross attention and decoder self-attention interactions to interactively acquire other roles’ critical information. The cross attention interaction aims to select other roles’ critical dialogue utterances, while the decoder self-attention interaction aims to obtain key information from other roles’ summaries. Experimental results have shown that our proposed method significantly outperforms strong baselines on two public role-oriented dialogue summarization datasets. Extensive analyses have demonstrated that other roles’ content could help generate summaries with more complete semantics and correct topic structures.

## introduction dialogue summarization aims at compressing the main content of a long conversation into a short text. with the development of online conversation tools, the amount and length of conversation are growing up rapidly. since a dialogue often contains complicated structure and ellipsis, it is time-consuming to read the whole dialogue. dialogue summarization thus becomes valuable since it could extract the key point of a conversation and greatly reduce the time cost. this technique is widely used in customer service @xcite , meeting @xcite , online chatting @xcite , etc. ## experiments 4.1 datasets there are two dialogue summarization datasets with role-oriented summarization tasks. thus, we evaluate the effectiveness of our proposed method on both datasets. first, we experiment on a chinese fine-grained customer service summarization dataset named csds foot_2 @xcite . it provides separate summaries for the user and the agent, and both may contain multiple topics. the other one is a chinese medical inquiry summarization dataset mc foot_3 @xcite . each dialogue has a summary of the patient's description and a summary of the doctor's suggestion. we note them as user summary and agent summary as well. most of the summaries in mc are extractive, and only a few are different from dialogue scripts. moreover, most dialogues in mc have only one topic. datasets, mc is easier to summarize while csds is more specific for role-oriented summarization and more challenging. the detailed statistics of the two datasets are given in ## conclusion and future work in this paper, we focus on the role-oriented dialogue summarization task. to fully exploit the information from other roles, we propose two role interaction methods on cross attention and decoder are transferred into number ids for calculation, and the period is used to split each summary into several sentences for rouge-l calculation. for bertscore, we use the official code 16 . for mover-score, we use moverscore-v2 17 and the bert-basechinese pretrained model for obtaining representations.