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).
@article{arxiv.2603.27358,
title = {Not Worth Mentioning? A Pilot Study on Salient Proposition Annotation},
author = {Amir Zeldes and Katherine Conhaim and Lauren Levine},
journal= {arXiv preprint arXiv:2603.27358},
year = {2026}
}