Towards Modeling Role-Aware Centrality for Dialogue Summarization (2022)

Abstract: Role-oriented dialogue summarization generates summaries for different roles in dialogue (e.g. doctor and patient). Existing methods consider roles separately where interactions among different roles are not fully explored. In this paper, we propose a novel Role-Aware Centrality (RAC) model to capture role interactions, which can be easily applied to any seq2seq models. The RAC assigns each role a specific sentence-level centrality score by involving role prompts to control what kind of summary to generate. The RAC measures both the importance of utterances and the relevance between roles and utterances. Then we use RAC to re-weight context representations, which are used by the decoder to generate role summaries. We verify RAC on two public benchmark datasets, CSDS and MC. Experimental results show that the proposed method achieves new state-of-the-art results on the two datasets. Extensive analyses have demonstrated that the role-aware centrality helps generate summaries more precisely.

## introduction the last few years have seen a land rush in research of generating summaries for dialogue such as meeting text and daily chatting due to the ever growing dialogue corpus from online conversation tools @xcite @xcite @xcite . typically, dialogue summarization aims at compressing the main content of a long conversation into a short text @xcite @xcite . different from traditional summarization tasks on document text, the main challenge of dialogue summarization is to summarize from utterances of multiple roles, who may have different opinions and interact with some of the other roles @xcite . * the authors contribute equally † contribution during internship at bytedance inc. ‡ corresponding author recently, @xcite pointed out that it is equally important to summarize the main content of each role in addition to the whole dialogue. thus, they proposed a more practical task: the role-oriented dialogue summarization, which aims at generating summaries for specified roles, e.g. user summary and agent summary. figure 1 shows an example of customer service and user dialogue about changing order delivery address. the roleoriented dialogue summarization generates summary for both user (e.g. user summary) and agent (e.g. agent summary). the two summaries are different in content and opinion. additionally, there is also an overall summary to summarize the whole dialogue. there are several methods focused on the roleoriented summarization task. @xcite trains different models for different role-oriented summaries by splitting their utterances, however, they ignore interactions between roles. @xcite proposed a role-interaction attention model. they modeled role-wise interactions through crossattention and self-attention in the decoder. how-ever, their method has to assign each role a specific decoder. in addition, the role-interaction has to be conducted between every two roles. that means both the model parameter and complexity increase with the number of roles. in this paper, we propose a novel role-aware centrality (rac) model for the role-oriented dialogue summarization task. centrality is widely used to measure the salience of sentences in a given document @xcite @xcite . the rac assigns each role a specific centrality. specifically, we first propose a role prompt that is attached to the start of the dialogue. the role prompt is used to guide what kind of summary to generate (i.e. user summary or agent summary). then we compute the centrality scores of each utterance. the final role-aware centrality is calculated by an interaction of role prompt and centrality scores. during decoding, we use the rac to reweight the dialogue context, which is used by the decoder to generate the summaries. we propose role prompts for each role together with the overall summary. in this way, different summaries can be modeled in a unified seq2seq framework. in addition, the rac can be easily applied to any sequence-to-sequence model with any number of roles. to evaluate the effectiveness of the rac, we apply the rac to three types of seq2seq structure: pgn, bertabs, and bart, and verify the models on two public chinese dialogue summarization datasets: csds and mc. experimental results show that our rac can improve all of their performance while accelerating the convergence of training. additionally, the rac based bart achieves new state-of-the-art performance on the two datasets. we summarize our contributions as follows: ## methodology in this section, we will introduce our proposed role-aware centrality (rac) model and the combination with the seq2seq structure. the main framework is shown in figure 2 . it consists of three components: bidirectional encoder, role-aware centrality model, and auto-regression decoder. ## conclusion in this paper, we bring the degree centrality into dialogue summarization and proposed a role-aware centrality (rac) model to capture role-interaction information. experiments on two datasets demonstrated that our proposed rac model is effective and achieved new state-of-the-art results. furthermore, our rac can models different kinds of summaries in a unified seq2seq framework without computational complexity increasing as roles increase. csds mc