@inproceedings{park-lee-2022-unsupervised,
title = "Unsupervised Abstractive Dialogue Summarization with Word Graphs and {POV} Conversion",
author = "Park, Seongmin and
Lee, Jihwa",
editor = "Hruschka, Estevam and
Mitchell, Tom and
Mladenic, Dunja and
Grobelnik, Marko and
Bhutani, Nikita",
booktitle = "Proceedings of the 2nd Workshop on Deriving Insights from User-Generated Text",
month = may,
year = "2022",
address = "(Hybrid) Dublin, Ireland, and Virtual",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wit-1.1",
doi = "10.18653/v1/2022.wit-1.1",
pages = "1--9",
abstract = "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.",
}
Markdown (Informal)
[Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion](https://aclanthology.org/2022.wit-1.1) (Park & Lee, WIT 2022)
ACL