# Restructuring Conversations using Discourse Relations for Zero-shot Abstractive Dialogue Summarization

arXiv: https://arxiv.org/abs/1902.01615 · HTML: https://mlsanity.com/paper/1902.01615/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2019.

## Built on by (most cited first, 2 of 2)

- [ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining](https://mlsanity.com/paper/2106.00829/) (2021)
- [Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization](https://mlsanity.com/paper/2209.00278/) (2022)

## Abstract (arXiv)

Dialogue summarization is a challenging problem due to the informal and unstructured nature of conversational data. Recent advances in abstractive summarization have been focused on data-hungry neural models and adapting these models to a new domain requires the availability of domain-specific manually annotated corpus created by linguistic experts. We propose a zero-shot abstractive dialogue summarization method that uses discourse relations to provide structure to conversations, and then uses an out-of-the-box document summarization model to create final summaries. Experiments on the AMI and ICSI meeting corpus, with document summarization models like PGN and BART, shows that our method improves the ROGUE score by up to 3 points, and even performs competitively against other state-of-the-art methods.