# DeepLENS: Deep Learning for Entity Summarization

arXiv: https://arxiv.org/abs/2003.03736 · HTML: https://mlsanity.com/paper/2003.03736/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [Characterizing Latent Perspectives of Media Houses Towards Public Figures](https://mlsanity.com/paper/2309.06112/) (2023)

## Abstract (arXiv)

Entity summarization has been a prominent task over knowledge graphs. While existing methods are mainly unsupervised, we present DeepLENS, a simple yet effective deep learning model where we exploit textual semantics for encoding triples and we score each candidate triple based on its interdependence on other triples. DeepLENS significantly outperformed existing methods on a public benchmark.