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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Accesso online: | https://arxiv.org/abs/2603.27358 |
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| _version_ | 1866914554984792064 |
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| author | Zeldes, Amir Conhaim, Katherine Levine, Lauren |
| author_facet | Zeldes, Amir Conhaim, Katherine Levine, Lauren |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_27358 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Not Worth Mentioning? A Pilot Study on Salient Proposition Annotation Zeldes, Amir Conhaim, Katherine Levine, Lauren Computation and Language 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). |
| title | Not Worth Mentioning? A Pilot Study on Salient Proposition Annotation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2603.27358 |