Not Worth Mentioning? A Pilot Study on Salient Proposition Annotation

arXiv cs.CL / 3/31/2026

💬 OpinionSignals & Early TrendsIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper notes that, while extractive summarization research focuses on recovering important propositions, graded proposition salience has been less operationalized in real-world data.
It adapts a graded summarization-based salience metric from the Salient Entity Extraction (SEE) line of work to quantify how salient propositions are.
The authors define a new proposition annotation task and apply it to a small, multi-genre dataset.
They assess annotator agreement and conduct a preliminary analysis linking the proposed salience metric to discourse unit centrality concepts used in RST-based discourse parsing.

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).