Coreference-Aware Dialogue Summarization

Summarizing conversations via neural approaches has been gaining research traction lately, yet it is still challenging to obtain practical solutions. Examples of such challenges include unstructured information exchange in dialogues, informal interactions between speakers, and dynamic role changes of speakers as the dialogue evolves. Many of such challenges result in complex coreference links. Therefore, in this work, we investigate different approaches to explicitly incorporate coreference information in neural abstractive dialogue summarization models to tackle the aforementioned challenges. Experimental results show that the proposed approaches achieve state-of-the-art performance, implying it is useful to utilize coreference information in dialogue summarization. Evaluation results on factual correctness suggest such coreferenceaware models are better at tracing the information Figure 1: An example of dialogue summarization: The flow among interlocutors and associating original conversation (in grey) is abbreviated; the summary accurate status/actions with the corresponding generated by a baseline model is in blue; the interlocutors and person mentions.