# Learning Feature Representations for Keyphrase Extraction

arXiv: https://arxiv.org/abs/1801.01768 · HTML: https://mlsanity.com/paper/1801.01768/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2018.

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

- [Cross-Domain Keyword Extraction with Keyness Patterns](https://mlsanity.com/paper/2409.18724/) (2024)

## Abstract (arXiv)

In supervised approaches for keyphrase extraction, a candidate phrase is encoded with a set of hand-crafted features and machine learning algorithms are trained to discriminate keyphrases from non-keyphrases. Although the manually-designed features have shown to work well in practice, feature engineering is a difficult process that requires expert knowledge and normally does not generalize well. In this paper, we present SurfKE, a feature learning framework that exploits the text itself to automatically discover patterns that keyphrases exhibit. Our model represents the document as a graph and automatically learns feature representation of phrases. The proposed model obtains remarkable improvements in performance over strong baselines.