Leveraging Non-dialogue Summaries for Dialogue Summarization (2022)

Abstract: To mitigate the lack of diverse dialogue summarization datasets in academia, we present methods to utilize non-dialogue summarization data for enhancing dialogue summarization systems. We apply transformations to document summarization data pairs to create training data that better befit dialogue summarization. The suggested transformations also retain desirable properties of non-dialogue datasets, such as improved faithfulness to the source text. We conduct extensive experiments across both English and Korean to verify our approach. Although absolute gains in ROUGE naturally plateau as more dialogue summarization samples are introduced, utilizing non-dialogue data for training significantly improves summarization performance in zero- and few-shot settings and enhances faithfulness across all training regimes.

## introduction dialogue summarization fundamentally differs from its non-dialogue counterparts in two ways: the presence of speaker information and the inherent abstractiveness that demands any dialogue summarization system to "read between the lines". consequently, training a dialogue summarization model requires datasets appropriate for the dialogue domain, which often calls for different provisions than those commonly found in traditional, non-dialogue summarization datasets. the bulk of research efforts in summarization, however, has historically been focused on written documents. as a result, the research community faces a shortage of diverse dialogue summarization data, in contrast to the abundance of non-dialogue summarization data @xcite . from such state of the literature, we identify a strong need for methods to utilize widely available non-dialogue summarization data in reinforcing dialogue summarization models. in this work, we present recipes to transform non-dialogue data into formats that enable direct integration into dialogue summarization training. during the transformation process, we also inherit desirable properties that arise from the extractiveness of non-dialogue summarization datasets. factual inconsistency and hallucination are major research problems in dialogue summarization @xcite @xcite . since extractive summaries naturally remain more faithful to the source text, we design our transformation schemes to retain such properties when adapting non-dialogue summary data to the dialogue domain. our contributions are as follows: in section 2, we first describe existing challenges in dialogue summarization. in section 3, we describe our dataset adaptation methods in detail. in section 4, we describe datasets, evaluation metrics, and experiments used to test our methods. ## conclusion we present simple but immediately effective methods to utilize abundant non-dialogue summarization data to improve dialogue summarization systems. we evaluate performance gains in similarity to reference summaries as well as in factual consistency to original transcript input. we find that our method is especially impactful in low-resource dialogue summarization. our research hints at two possible avenues for further investigation: reinforcing the three presented transformation recipes with a more methodical generation of prompts @xcite , or introducing new transformations that better capture the unique properties of dialogue summarization datasets.