Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization (2022)

Abstract: In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them. We present SICK, a framework that uses commonsense inferences as additional context. Compared to previous work that solely relies on the input dialogue, SICK uses an external knowledge model to generate a rich set of commonsense inferences and selects the most probable one with a similarity-based selection method. Built upon SICK, SICK++ utilizes commonsense as supervision, where the task of generating commonsense inferences is added upon summarizing the dialogue in a multi-task learning setting. Experimental results show that with injected commonsense knowledge, our framework generates more informative and consistent summaries than existing methods.

## introduction abstractive dialogue summarization is a task of generating a shorter summary while preserving the context of a conversation @xcite . unlike conventional documentto-document summarization (e.g., news articles and scientific publications) @xcite , such dialogue-to-document summarization suffers from the discrepancy between input and output forms, which makes learning their mapping patterns more challenging. there are two key challenges that make summarizing dialogues harder than documents. first, detecting unspoken intention is crucial for understanding an utterance @xcite . as shown in figure 1 , without understanding the intent "to make fun of someone", it is hard to write a correct summary. second, there exists information that can only be understood when figure 1 : example of dialogue-summary pairs. capturing the intention and hidden meaning is important to generate a novel summary. commonsense knowledge models @xcite @xcite such as comet can generate a set of event-centered (e.g., hinderedby, xreason, xneed) and socialinteraction (e.g., xintent, xwant) commonsense inferences. we argue that the aforementioned issues can be mitigated using commonsense knowledge by filling in the gap in a dialogue. despite its effectiveness, it is non-trivial to use commonsense knowledge for improving abstractive dialogue summarization performance. while commonsense knowledge has been widely applied to commonsense reasoning @xcite @xcite @xcite ) or question answering @xcite , its usage for summarization is understudied @xcite . in this paper, we present our framework sick and its extension sick++ to properly inject commonsense knowledge into state-of-the-art language models (e.g., bart @xcite ) for abstractive dialogue summarization. we argue a naïve adoption of commonsense only hurts performance in summarization, as (a) expanding source contents is counter-intuitive approach for the goal of condensation, and (b) simply adding additional inputs in pre-trained language models does not lead to robust inferences as reported in @xcite . our framework addresses this by (a) filtering and (b) robust training. based on analytical measurements, commonsense knowledge is selected and enumerated as an additional context of dialogue inputs. in sick++, we also design a new auxiliary task named commonsense supervision. using commonsense knowledge generated from gold summaries as additional supervision, the goal of the task is to generate the target commonsense. then, the dialogue summarization and commonsense generation tasks are jointly learned in a multi-task learning setting to effectively inject commonsense knowledge into the shared encoder. to validate our framework, we conduct a set of experiments on abstractive dialogue summarization benchmarks. empirical results show that our framework can improve summarization performance with leveraged commonsense knowledge, outperforming other baselines. human evaluation results prove that our method can generate informative and consistent summaries. in addition, we conduct experiments to analyze the effect of commonsense knowledge on abstractive dialogue summarization. ## proposed framework in this section, to inject commonsense knowledge for rich abstractive dialogue summarization, we introduce our new framework, sick(summarizing with injected commonsense knowledge) and its extension sick++, as shown in figure 2 . ## analysis to evaluate the effectiveness of our method, we address the following research questions to guide our experiments: ## conclusion in this work, we propose sick and sick++ framework in order to resolve the two key challenges: i) filling in the gap in dialogues; ii) injecting commonsense knowledge into a model. we show that the difficulties in dialogues are resolved with commonsense knowledge and demonstrated that our framework can successfully inject commonsense knowledge. as a result of injected commonsense knowledge, we obtain competitive results on sam-sum and dialogsum benchmarks. for comet, we use bart version among several available versions. publicly available checkpoints were used for both comet foot_2 and para-comet foot_3 .gpt-2 version was used for para-comet.for inference, we use beam search with beam size 5 and 10 for each comet and para-comet, the default setting provided in the public repository. all this procedure is done on one geforce rtx 3090 gpu. to investigate the overhead, we measure the time required to generate commonsense inferences in samsum. samsum, consisted of 14732 samples within the train subset, took 18.3 hours to generate all the needed commonsense inferences. in other words , it took about 4.4719 seconds per dialogue to generate the commonsense.note that samsum has an average of 11.2 turns per dialogue, so that this number could vary depending on how long the given dialogue is.