Summary of Newsedits 2.0: Learning the Intentions Behind Updating News, by Alexander Spangher et al.
NewsEdits 2.0: Learning the Intentions Behind Updating News
by Alexander Spangher, Kung-Hsiang Huang, Hyundong Cho, Jonathan May
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Digital Libraries (cs.DL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a method for predicting which facts in news articles are likely to be updated based solely on the text of the article. The authors hypothesize that linguistic features can indicate factual fluidity and develop an ensemble model to predict fact updates with high precision. They introduce the NewsEdits 2.0 taxonomy, an edit-intentions schema that separates fact updates from stylistic and narrative updates in news writing. To demonstrate the usefulness of these findings, the authors construct a language model question asking (LLM-QA) abstention task, showing that LLM abstinence reaches near oracle levels of accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting which facts in news articles will change over time. The researchers want to find out if they can tell which facts are going to be updated just by reading the article itself, without looking at anything else. They think that certain words or phrases in the text might give it away, and they test this idea using a big collection of old and new news articles. They also create a special system for categorizing what kind of changes are happening in the articles – like whether it’s just wording changing or actual facts being updated. This helps them build a really good model that can predict when facts will change with surprising accuracy. |
Keywords
» Artificial intelligence » Ensemble model » Language model » Precision
