  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
coreference-aware models are better at tracing the information flow among
interlocutors and associating accurate status/actions with the corresponding
interlocutors and person mentions.
