cff-version: 1.2.0
abstract: "<p>Code used to generate summaries that preserve the moral framing of the original news article. We leverage the zero-shot summarization ability of Large Language Models, shown to produce results on par with human-generated summaries. We compare three language models and five prompting methods. Leveraging the intuition that journalists intentionally use or report moral-laden words in the article text, we propose approaches that first identify moral-laden words in the article (e.g., through Chain-of-Thought or supervised classification) and then guide the language model in preserving such words in the summary.</p>"
authors:
  - family-names: Liscio
    given-names: Enrico
    orcid: "https://orcid.org/0000-0002-8285-5867"
  - family-names: Lorandi
    given-names: Michela
    orcid: "https://orcid.org/0000-0002-6131-8763"
  - family-names: Murukannaiah
    given-names: Pradeep K.
    orcid: "https://orcid.org/0000-0002-1261-6908"
title: "News is More than a Collection of Facts: Moral Frame Preserving News Summarization - code"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/7b387539-0f9b-4143-b960-76eeef8886ab.v1
license: MIT
date-released: 2025-07-30