# Not Worth Mentioning? A Pilot Study on Salient Proposition Annotation

arXiv: https://arxiv.org/abs/2603.27358 · HTML: https://mlsanity.com/paper/2603.27358/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 28 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://mlsanity.com/paper/1802.08636/) (2018)
- [Neural Summarization by Extracting Sentences and Words](https://mlsanity.com/paper/1603.07252/) (2016)
- [GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation](https://mlsanity.com/paper/2306.01966/) (2023)
- [Automatic Event Salience Identification](https://mlsanity.com/paper/1809.00647/) (2018)
- [GUMsley: Evaluating Entity Salience in Summarization for 12 English Genres](https://mlsanity.com/paper/2401.17974/) (2024)
- [GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction](https://mlsanity.com/paper/2504.10792/) (2025)
- [Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs](https://mlsanity.com/paper/2407.04855/) (2024)

## Built on by (most cited first, 2 of 2)

- [Expect the Unexpected? Testing the Surprisal of Salient Entities](https://mlsanity.com/paper/2604.10724/) (2026)
- [The Sum of All Summaries: PROPALIGN for Graded Proposition Salience Evaluation](https://mlsanity.com/paper/2610.19387/) (2026)

## Abstract (arXiv)

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).