Focused Meeting Summarization via Unsupervised Relation Extraction

TL;DR

This paper introduces an unsupervised relation extraction framework for focused meeting summarization, outperforming existing unsupervised methods and rivaling supervised approaches in quality.

Contribution

It adapts a relation learner with task-specific constraints for meeting summarization, a novel approach in this domain.

Findings

Outperforms unsupervised extractive baselines

Competitive with supervised methods in ROUGE scores

Effective in decision summarization tasks

Abstract

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.