Not Worth Mentioning? A Pilot Study on Salient Proposition Annotation
This work provides a preliminary operationalization of proposition salience for extractive summarization, but the contribution is incremental due to the small dataset and lack of concrete performance gains.
The paper introduces a graded proposition salience metric adapted from Salient Entity Extraction (SEE) and applies it to a small multi-genre dataset, evaluating annotator agreement and exploring its correlation with discourse unit centrality in RST-based parsing.
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).