Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion

TL;DR

This paper introduces an unsupervised method for abstractive dialogue summarization using word graphs, demonstrating robustness across diverse datasets and providing a reproducible baseline for future research.

Contribution

It presents a novel approach utilizing multi-sentence compression graphs with simple path-reranking and topic segmentation, advancing unsupervised dialogue summarization.

Findings

Effective across multiple domains including meetings and interviews

Demonstrates robustness and reliability of the method

Provides open-source code for reproducibility

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.