# Improving Abstractive Dialogue Summarization Using Keyword Extraction
###### Abstract  
Abstractive dialogue summarization aims to generate a short passage that contains important content for a particular dialogue spoken by multiple speakers. In abstractive dialogue summarization systems, capturing the subject in the dialogue is challenging owing to the properties of colloquial texts. Moreover, the system often generates uninformative summaries. In this paper, we propose a novel keyword-aware dialogue summarization system (KADS) that easily captures the subject in the dialogue to alleviate the problem mentioned above through the efficient usage of keywords. Specifically, we first extract the keywords from the input dialogue using a pre-trained keyword extractor. Subsequently, KADS efficiently leverages the keywords information of the dialogue to the transformer-based dialogue system by using the pre-trained keyword extractor. Extensive experiments performed on three benchmark datasets show that the proposed method outperforms the baseline system. Additionally, we demonstrate that the proposed keyword-aware dialogue summarization system exhibits a high-performance gain in low-resource conditions where the number of training examples is highly limited.  
abstractive summarization; dialogue summarization; keyword extraction +
Footnote †: journal: _Sci_  
0000-0002-2300-3000-3000-3000-3000-3000-3000-3000-3000-3000-3000-3000-3000-3000-3000-3000-3000-3000-3000-30000-3000-30000-3  
To solve such problems in the dialogue summarization task, we propose a keyword-aware dialogue summarization system (KADS) that efficiently utilizes the keyword information using a keyword extractor. By leveraging the keyword information to the summarizer, we adopt the advantage of extractive summarization systems to the abstractive summarization systems. KADS first extracts the keywords from a dialogue using a state-of-the-art pre-trained keyword extractor such as keyBERT [10]. Then, we construct the input text by prepending extracted keywords after a special token <_keyword>_ and inserting a segment token <_/s>_ before the dialogue text. And then we fine-tune the pre-trained encoder-decoder models like BART [11] to generate the summary of the given dialogue with the help of the keywords we added. Experimental results on three widely used dialogue summarization benchmark datasets show that our proposed KADS shows significant improvement over the baseline systems in ROUGE [12] metric, with a gain of about 2.7% in ROUGE-L. Also, through the qualitative analysis, we find that extracted keywords efficiently assist the system in generating main words, as shown in the summary generated by our system in Table 1. In this example, we can infer that keywords _\"degree\"_ and _\"responsible\"_ assist in generating the words _\"major\"_ and _\"reason to the job\"_. Furthermore, we explore the usage of various keyword extractors on our system to find the best keyword extractor for dialogue summarization. Finally, we validate the performance of our keyword-aware summarization system in low-resource conditions where the number of the dataset is scarce, which often occurs in the dialogue summarization task. We demonstrate that our method is even more effective with larger performance improvement than baseline systems in these low-resource conditions. The main contributions of this study can be summarized as follows:  
* We propose a novel keyword-aware abstractive summarization system that efficiently leverages the key information in a dialogue.
* We demonstrate that our proposed keyword-aware method outperforms baseline methods in three benchmark datasets.
* We explore the usage of various keyword extractors for dialogue summarization tasks to find the best usage.
* We demonstrate the effectiveness of the proposed keyword-aware method in low-resource conditions.