Topic Paraphrasing Model for Abstractive Dialogue Summarization
Zhizhuo Yang, Wei Zhang · 2024
Abstractive Dialogue summarization is a challenging task due to its dynamic interaction among multiple speakers and lack of sufficient training data. Existing methods treat the dialogue as a linear sequence of utterances while ignoring the diverse topic relations between each utterance. Besides, the limited labeled data further hinders the ability of data hungry neural models. Therefore, we propose a novel Topic Paraphrasing Model (TPDSM) for abstractive dialogue summarization. It adopts cross attention to interactively acquire other topic critical information. Moreover, we construct a topic Paraphrasing corpus between topic and narrative sentence from existing datasets. Extensive experiments on benchmark datasets demonstrate that the proposed model significantly outperforms strong baselines and achieves new state-of-the-art performance.