Not Worth Mentioning? A Pilot Study on Salient Proposition Annotation

TL;DR

This study introduces a new metric for graded proposition salience in summarization, adapting existing methods to natural data, and explores its relationship with discourse centrality in RST.

Contribution

It operationalizes graded proposition salience, applies it to a new dataset, and investigates its connection to discourse unit importance.

Findings

Proposed a new graded salience metric for propositions.

Applied the metric to a multi-genre dataset.

Explored the relationship between salience and discourse centrality.

Abstract

Despite a long tradition of work on extractive summarization, which by nature aims to recover the most important propositions in a text, little work has been done on operationalizing graded proposition salience in naturally occurring data. In this paper, we adopt graded summarization-based salience as a metric from previous work on Salient Entity Extraction (SEE) and adapt it to quantify proposition salience. We define the annotation task, apply it to a small multi-genre dataset, evaluate agreement and carry out a preliminary study of the relationship between our metric and notions of discourse unit centrality in discourse parsing following Rhetorical Structure Theory (RST).