Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion
Seongmin Park, Jihwa Lee · 2022
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 heuristicbased system with deep learning.We opensource our code 1 , to provide a strong, reproducible baseline for future research into unsupervised dialogue summarization.