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# GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction

**Authors:** [Jessica Lin](https://md.mova.elamur.ai/free/en/docs/papers/authors/jessica-lin/), [Amir Zeldes](https://md.mova.elamur.ai/free/en/docs/papers/authors/amir-zeldes/) · **Year:** 2025 · **Venue:** Findings of ACL 2025 **Link:** [aclanthology.org/2025.findings-acl.24](https://aclanthology.org/2025.findings-acl.24/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (paper); GUM annotations are CC BY(-NC) by part — evaluation only

[**1** citations](https://openalex.org/works?filter=cites:W4412888926) openalex-snapshot · 2026-10-14[**6** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lin-2025-gum-sage-novel-dataset-approach/) of the 140673 papers whose bibliographies we read[Findings of the Association for Computational Linguistics: ACL 2025](https://aclanthology.org/volumes/2025.findings-acl/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2025.findings-acl.24/)
- [PDF](https://aclanthology.org/2025.findings-acl.24.pdf)
- [DOI 10.18653/v1/2025.findings-acl.24](https://doi.org/10.18653/v1/2025.findings-acl.24)
- [arXiv 2504.10792](https://arxiv.org/abs/2504.10792)
- [OpenAlex](https://openalex.org/W4412888926)

**Authors (2)**

1. [Jessica Lin](https://openalex.org/A5114031547) h 11 · 392 citations · 41 works
2. [Amir Zeldes](https://openalex.org/A5089212858) [ORCID](https://orcid.org/0000-0001-8016-6753) · h 16 · 1524 citations · 238 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

Salience is defined operationally: an entity is as salient as the number of one-sentence summaries of the document (out of five) that mention it, giving a 0–5 score for every named and unnamed entity of GUM's 213 documents in 12 genres. Training documents get one expert summary and four LLM summaries; dev and test get human summaries and manual alignment. Mentions are aligned to summaries by an ensemble of string match, a coreference model run over document plus summary, and GPT-4o judgements, combined by logistic regression with entity type, genre and position. On average 13.8% of entities are salient at all, 2.1% score 5. Predicting salience from the document alone, the summary-based approach correlates best with the human scores (Spearman 0.54); GPT-4o prompted directly reaches only 0.23–0.25 and over-predicts mid scores, early entities and persons/places, and does worst on abstract, event and time entities. A position baseline (earlier first mention = more salient) is included.

## Ideas

- I1. Compute a document-level salience score from Mova's own chains, with no learning: chain length, position of the first mention, share of mentions in subject position, presence in the first sentence or title. Score it against GUM-SAGE on GUM dev (Spearman, top-1/top-3 F1) next to the paper's position baseline, using `coref` output on gold and SLM trees. A measure the coref work currently lacks: which chains matter, not only whether links are right.
- I2. Use that global salience in pronoun resolution as a tie-breaker after agreement and Hobbs order: the frame reader's `Ctx` already uses local salience (best role per sentence, halved per sentence); a small bonus for the document's top entity would help *he/she* in long stretches about the protagonist. Measure he/she/his precision on GUM dev and frame bench coref F1.
- I3. In the student, weight remembered frames by the salience of their entities so that options about the protagonist outrank options about a passing character when both are supported (step «right option outscored» in [Watching the SLM think: the diagnosis of its answers](https://md.mova.elamur.ai/free/en/docs/docs/mmm-diagnosis/), 21% on the Torah course). Measure the course test counts with the weight on and off.
- I4. For lessons the teacher can write a one-sentence summary once per lesson; entities present in it become the salient set, exactly the paper's definition. A cheap silver signal for I3 without per-entity labelling; check its agreement with I1 on a few lessons first.

## For Mova

[35. Reference and coreference](https://md.mova.elamur.ai/free/en/docs/languages/en/35-reference-and-coreference/) §2.4 (salience in practice), §10. Code: `coref/src/eval.rs` (salience score and its evaluation), `prag/src/frames.rs` (`Ctx` salience), `world/src/knowledge.rs` (weights). Measures: Spearman with GUM-SAGE on dev, pronoun precision on GUM dev, frame bench coref F1, course test counts.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2025.findings-acl.24](https://doi.org/10.18653/v1/2025.findings-acl.24) · [arXiv 2504.10792](https://arxiv.org/abs/2504.10792)

```
@inproceedings{lin2025gum,
  title = {{GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction}},
  author = {Lin, Jessica and Zeldes, Amir},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2025},
  year = {2025},
  doi = {10.18653/v1/2025.findings-acl.24},
  eprint = {2504.10792},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2025.findings-acl.24}
}
```

- [← previous Predicting scalar inferences from "or" to "not both" using neural sentence encoders](https://md.mova.elamur.ai/free/en/docs/papers/li-2021-predicting-scalar-inferences-or-not/)
- [next → Multiword expressions: linguistic precision and reusability](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2002-copestake-multiword-expressions-linguistic-precision-reusability/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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