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.
@article{arxiv.2205.13108,
title = {Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion},
author = {Seongmin Park and Jihwa Lee},
journal= {arXiv preprint arXiv:2205.13108},
year = {2022}
}