  We advance the state-of-the-art in unsupervised abstractive dialogue
summarization by utilizing multi-sentence compression graphs. Starting from
well-founded assumptions about word graphs, we present simple but reliable
path-reranking and topic segmentation schemes. Robustness of our method is
demonstrated on datasets across multiple domains, including meetings,
interviews, movie scripts, and day-to-day conversations. We also identify
possible avenues to augment our heuristic-based system with deep learning. We
open-source our code, to provide a strong, reproducible baseline for future
research into unsupervised dialogue summarization.
