RepSum: Unsupervised Dialogue Summarization based on Replacement Strategy

In the field of dialogue summarization, due to the lack of training data, it is often difficult for supervised summary generation methods to learn vital information from dialogue context. Several works on unsupervised summarization for document by leveraging semantic information solely or auto-encoder strategy (i.e., sentence compression), they however cannot be adapted to the dialogue scene due to the limited words in utterances and huge gap between the dialogue and its summary. In this study, we propose a novel unsupervised strategy to address this challenge, which roots from the hypothetical foundation that a superior summary approximates a replacement of the original dialogue, and they are roughly equivalent for auxiliary (self-supervised) tasks, e.g., dialogue generation. The proposed strategy Rep-Sum is applied to generate both extractive and abstractive summary with the guidance of the followed n th utterance generation and classification tasks. Extensive experiments on various datasets demonstrate the superiority of the proposed model compared with other unsupervised methods.