# SWAT: A System for Detecting Salient Wikipedia Entities in Texts

arXiv: https://arxiv.org/abs/1804.03580 · HTML: https://mlsanity.com/paper/1804.03580/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2018.

## Built on by (most cited first, 3 of 3)

- [Towards Self-Contained Answers: Entity-Based Answer Rewriting in Conversational Search](https://mlsanity.com/paper/2403.01747/) (2024)
- [Leveraging Contextual Information for Effective Entity Salience Detection](https://mlsanity.com/paper/2309.07990/) (2023)
- [Scalable Detection of Salient Entities in News Articles](https://mlsanity.com/paper/2405.20461/) (2024)

## Abstract (arXiv)

We study the problem of entity salience by proposing the design and implementation of SWAT, a system that identifies the salient Wikipedia entities occurring in an input document. SWAT consists of several modules that are able to detect and classify on-the-fly Wikipedia entities as salient or not, based on a large number of syntactic, semantic and latent features properly extracted via a supervised process which has been trained over millions of examples drawn from the New York Times corpus. The validation process is performed through a large experimental assessment, eventually showing that SWAT improves known solutions over all publicly available datasets. We release SWAT via an API that we describe and comment in the paper in order to ease its use in other software.