A Relation Enhanced Model For Abstractive Dialogue Summarization

Abstract

Traditional document summarization models perform less satisfactorily on dialogues due to the complex personal pronouns referential relationships and insufficient modeling of conversation. To address this problem, we propose a novel end-to-end Transformer-based model for abstractive dialogue summarization with Relation Enhanced method based on BART named RE-BART. Our model leverages local relation and global relation in a conversation to model dialogue and to generate better summaries. In detail, we consider that the verb and related arguments in a single utterance contribute to the local event for encoding the dialogue. And coreference information in a whole conversation represents the global relation which helps to trace the topic and information flow of the speakers. Then we design a dialogue relation enhanced model for modeling both information. Experiments on the SAMsum dataset show that our model outperforms various dialogue summarization approaches and achieves new state-of- the-art ROUGE results.