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Role-oriented dialogue summarization is to generate summaries for differentroles in the dialogue, e.g., merchants and consumers. Existing methods handlethis task by summarizing each role's content separately and thus are prone toignore the information from other roles. However, we believe that other roles'content could benefit the quality of summaries, such as the omitted informationmentioned by other roles. Therefore, we propose a novel role interactionenhanced method for role-oriented dialogue summarization. It adopts crossattention and decoder self-attention interactions to interactively acquireother roles' critical information. The cross attention interaction aims toselect other roles' critical dialogue utterances, while the decoderself-attention interaction aims to obtain key information from other roles'summaries. Experimental results have shown that our proposed methodsignificantly outperforms strong baselines on two public role-oriented dialoguesummarization datasets. Extensive analyses have demonstrated that other roles'content could help generate summaries with more complete semantics and correcttopic structures.

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