Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality (2023)

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 in both global level and local level. 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.

## introduction online conversations have become essential to communication in our daily work and life. due to the information explosion, dialogue summarization has become a vivid field of research in recent years, which is meaningful for many applications, e.g. online customer service @xcite and meeting summary @xcite . dialogue summarization aims to condense crucial information in a long dialogue into a short text like traditional summarization tasks. differently, the main challenges of dialogue summarization are the viewpoints of multiple speaker roles @xcite @xcite and shifting topics @xcite @xcite during the conversation process. as shown in figure 1 , summaries not only depend on the overall context but also needs the identification and selection of salient context in crucial sub-topics. we can see that the blue text in summaries is about sub-topic #1 "the reason why the product is shipped yet" and the orange text is about sub-topic #2 "the user decided to refund", which are aligned to the two salient sub-topics from dialogue utterances in the first and second block. the sub-topic #3 is useless for summaries. this example shows the necessity to model the salient context and sub-topics in the dialogue. in this paper, we propose a novel topic-aware global-local centrality (glc) model to select salient contexts from all sub-topics. the centrality is an effective technique to measure the importance of sentences in a given document from unsupervised extractive summarization @xcite @xcite . the glc contains global-and local-level centrality, which are used to capture the salience of sub-topics and content in each sub-topic respectively. based on these centralities, we can guide the model to focus on the salient context and sub-topics when generating summaries. specifically, we employ utterance-level representations to cluster utterances and obtain sub-topic centers and assign each utterance to one sub-topic. then, we compute the global centrality over subtopic centers to measure the importance of each sub-topic and the local centrality over utterances of each sub-topic to measure the importance of sub-topic content. finally, we combine the two to re-weight the dialogue context representations for the decoder to generate summaries. to evaluate the effectiveness of our proposed glc, we apply the glc to three different types of seq2seq structure: pgn, bertabs, and bart, and verify them on three public dialogue summarization datasets: csds, mc, and samsum. csds and mc are two chinese role-oriented summarization datasets that not only need to generate the overall summary of the dialogue but also need to generate role-oriented summaries for specific speakers in the dialogue as shown in figure 1 . samsum is a widely used english dialogue summarization dataset. to generate role-oriented summaries, in this paper, we directly employ role prompts to guide the model to generate proper summaries. and the representations of role prompts can add role information to the centrality computation. experimental results show that our glc can improve the performance of all these seq2seq structures on three datasets. and the glc-based bart model obtains new state-of-the-art results on the csds and mc. our contributions can be summarized as 1) we propose a novel topic-aware global-local centrality (glc) model to guide the model to identify the salient contexts and sub-topics in the dialogue. 2) our glc can bring improvement to different seq2seq models by easily plugging in and does not add any extra parameters to the seq2seq models. 3) the glc-based bart model achieves new state-of-the-art results on csds and mc. besides, extension studies prove our glc can effectively capture vital sub-topics. ## discussion we conduct many external experiments on the csds dataset to further analyze the effectiveness of our proposed glc. and more discussions are shown in the appendix. ## related work dialogue summarization has caught more and more attention in recent years and is widely used in various domains, e.g. meeting summarization @xcite , daily dialogue summarization @xcite @xcite , etc. different from traditional summarization tasks, dialogue summarization needs to identify the role of speakers and capture the change of sub-topics during the dialogue. besides, the dialogue summarization task has less labeled data and longer inputs. all of these make dialogue summarization harder to solve @xcite @xcite . recent dialogue summarization models can be categorized into three types: 1) data augmentation methods @xcite @xcite , which attempt to construct more pseudo-data to train a better model; 2) topicbased models @xcite @xcite , which track the change of topic information in the dialogue to generate more focused summary; and 3) semantic structure-based models @xcite @xcite @xcite , which employs semantic structures to enhance the summarization model. however, they ignored the sub-topics information in the dialogue utterances, which is crucial for dialogue summarization. recently, zhao et al. (2020) modified the attention mechanism to focus on the topic words, which can force the model to learn the topic information. zou et al. (2021) employed neural topic model to model the global level topic information. @xcite tried to model the change of sub-topics by introducing contrastive learning. differently, in this paper, we bring the centrality, that has been widely used in unsupervised summarization @xcite @xcite , into the dialogue summarization task and proposed a novel topicaware global-local centrality model to capture salient dialogue utterances and sub-topics at the same time. our proposed method is effective and more flexible. ## conclusion in this paper, we bring the centrality into dialogue summarization tasks and proposed a novel topicaware global-local centrality (glc) model for better capturing the sub-topic information in the dialogue utterances. our glc can be easily applied to any seq2seq structure and bring improvement to their performance. experiments and further analysis demonstrated that glc can effectively identify vital sub-topics and salient content in the dialogue. in future work, we will try to extend our work to datasets with longer inputs.