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Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion

2022-05-26 02:18:12
Seongmin Park, Jihwa Lee

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.

Abstract (translated)

URL

https://arxiv.org/abs/2205.13108

PDF

https://arxiv.org/pdf/2205.13108.pdf


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