Not Worth Mentioning? A Pilot Study on Salient Proposition Annotation
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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).
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 28 Mar 2026.
Builds on
8 of 26 references.
References · 8
- 2016SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documentscited by 245
- 2018Ranking Sentences for Extractive Summarization with Reinforcement Learningcited by 128
- 2016Neural Summarization by Extracting Sentences and Wordscited by 114
- 2023GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluationcited by 17
- 2018Automatic Event Salience Identificationcited by 14
- 2025GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Predictioncited by 6
- 2024GUMsley: Evaluating Entity Salience in Summarization for 12 English Genrescited by 5
- 2024Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMscited by 3
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Citing papers per year: 2026: 1.
1 paper.