# Finding News Citations for Wikipedia

## TL;DR

This paper presents a supervised two-stage approach to identify and recommend appropriate news citations for Wikipedia statements, improving citation accuracy and coverage at scale.

## Contribution

It introduces a novel classification and citation recommendation framework specifically for news citations in Wikipedia, incorporating properties like entailment, centrality, and authority.

## Key findings

- High precision in citation recommendation
- Effective classification of statements needing news citations
- Scalable approach tested on 20 million articles

## Abstract

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 In many cases citations are either outdated or missing altogether.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.