# Focused Meeting Summarization via Unsupervised Relation Extraction

arXiv: https://arxiv.org/abs/1606.07849 · HTML: https://mlsanity.com/paper/1606.07849/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2016.

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

- [Decision-Focused Summarization](https://mlsanity.com/paper/2109.06896/) (2021)
- [Abstractive Meeting Summarization: A Survey](https://mlsanity.com/paper/2208.04163/) (2022)

## Abstract (arXiv)

We present a novel unsupervised framework for focused meeting summarization that views the problem as an instance of relation extraction. We adapt an existing in-domain relation learner (Chen et al., 2011) by exploiting a set of task-specific constraints and features. We evaluate the approach on a decision summarization task and show that it outperforms unsupervised utterance-level extractive summarization baselines as well as an existing generic relation-extraction-based summarization method. Moreover, our approach produces summaries competitive with those generated by supervised methods in terms of the standard ROUGE score.