# Finding News Citations for Wikipedia

arXiv: https://arxiv.org/abs/1703.10339 · HTML: https://mlsanity.com/paper/1703.10339/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2017.

## Built on by (most cited first, 5 of 5)

- [A Survey of Large Language Models Attribution](https://mlsanity.com/paper/2311.03731/) (2023)
- [Unifying Corroborative and Contributive Attributions in Large Language Models](https://mlsanity.com/paper/2311.12233/) (2023)
- [Assessing the quality of sources in Wikidata across languages: a hybrid approach](https://mlsanity.com/paper/2109.09405/) (2021)
- [Who Are All The Stochastic Parrots Imitating? They Should Tell Us!](https://mlsanity.com/paper/2310.10583/) (2023)
- [WINELL: Wikipedia Never-Ending Updating with LLM Agents](https://mlsanity.com/paper/2508.03728/) (2025)

## Abstract (arXiv)

An important editing policy in Wikipedia is to provide citations for added statements in Wikipedia pages, where statements can be arbitrary pieces of text, ranging from a sentence to a paragraph. In many cases citations are either outdated or missing altogether. In this work we address the problem of finding and updating news citations for statements in entity pages. We propose a two-stage supervised approach for this problem. In the first step, we construct a classifier to find out whether statements need a news citation or other kinds of citations (web, book, journal, etc.). In the second step, we develop a news citation algorithm for Wikipedia statements, which recommends appropriate citations from a given news collection. Apart from IR techniques that use the statement to query the news collection, we also formalize three properties of an appropriate citation, namely: (i) the citation should entail the Wikipedia statement, (ii) the statement should be central to the citation, and (iii) the citation should be from an authoritative source. We perform an extensive evaluation of both steps, using 20 million articles from a real-world news collection. Our results are quite promising, and show that we can perform this task with high precision and at scale.