---
title: "Frame In, Frame Out — Pastorino 2026"
description: "Standard summarization metrics check factuality, coherence and coverage."
authors: ["Valeria Pastorino", "Nafise Sadat Moosavi"]
year: 2026
venue: "Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026)"
source: "https://aclanthology.org/2026.starsem-conference.25/"
doi: "10.18653/v1/2026.starsem-conference.25"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/pastorino-2026-frame-frame-out-measuring-framing/"
---
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# Frame In, Frame Out: Measuring Framing Bias in LLM-Generated News Summaries

**Authors:** Valeria Pastorino, [Nafise Sadat Moosavi](https://md.research.elamur.ai/free/en/authors/nafise-sadat-moosavi/) · **Year:** 2026 · **Venue:** Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (\*SEM 2026) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.starsem-conference.25/) · [PDF](https://aclanthology.org/2026.starsem-conference.25.pdf) · [DOI 10.18653/v1/2026.starsem-conference.25](https://doi.org/10.18653/v1/2026.starsem-conference.25) · [OpenAlex](https://openalex.org/W4416091719)

**Citations:** 3 citations (openalex-snapshot · 2026-10-14)

## Summary
Standard summarization metrics check factuality, coherence and coverage. They do not check whether a summary adds an interpretive slant, or framing, through emphasis, omission or evaluative wording. The authors present FIFO (Frame In, Frame Out), described as the first large-scale benchmark for detecting framing in LLM-generated news summaries. It is built on XSum, a corpus of BBC articles with single-sentence reference summaries.

Framing is treated as a binary property: Framed or Not Framed. The dataset has two layers:
- **Silver labels:** 15,499 outputs from 27 summarization systems, labeled by a majority-vote jury of three LLMs.
- **Gold labels:** 320 summaries labeled by experts, used to validate the jury and to derive calibration weights that correct jury errors when computing framing rates.

Larger LLMs such as GPT-4, Claude and LLaMA often frame more than the human references. Smaller or XSum-fine-tuned models frame less, partly because their output quality is lower. Framing rates also depend strongly on topic, and they rise even in science and health news. The work argues that framing should become a standard dimension of summarization evaluation.

## Key points
- **Data and annotation:** FIFO pairs 15,499 jury-labeled summaries with 320 expert-labeled ones.
  - The jury consists of GPT-4.1-nano, GPT-4o and GPT-3.5-Turbo.
  - Agreement between the jury and the experts is Cohen's κ = 0.616.
  - Labels and checkpoints are released at github.com/vpastorino/FIFO.
- **Calibration:** Jury labels are mapped to expert-derived scores, 0.778 for Framed and 0.163 for Not Framed. Each system's framing rate is the mean of these scores rather than of the raw binary labels.
- **Model effects:**
  - Larger models frame significantly more than smaller ones (p = 0.0012).
  - XSum fine-tuning lowers framing (p = 0.0006; 95% CI −19.27% to −7.78%).
  - Within a model family, size correlates weakly and negatively with framing (r = −0.44).
- **Topic effects:** Human references frame about 53% of Political News and 31% of Health & Science items. Many high-capacity models exceed both baselines.
- **Length:** Longer summaries are modestly more likely to be framed (point-biserial r ≈ 0.19). Framed summaries average 147 words, compared with 83 for Not Framed ones.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{pastorino2026frame,
  title = {{Frame In, Frame Out: Measuring Framing Bias in LLM-Generated News Summaries}},
  author = {Pastorino, Valeria and Moosavi, Nafise Sadat},
  booktitle = {Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.starsem-conference.25},
  url = {https://doi.org/10.18653/v1/2026.starsem-conference.25}
}
```

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