# Keyphrase Extraction using Sequential Labeling

arXiv: https://arxiv.org/abs/1608.00329 · HTML: https://mlsanity.com/paper/1608.00329/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2016.

## Built on by (most cited first, 2 of 2)

- [KPDrop: Improving Absent Keyphrase Generation](https://mlsanity.com/paper/2112.01476/) (2021)
- [Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and Dissertations](https://mlsanity.com/paper/2107.00516/) (2021)

## Abstract (arXiv)

Keyphrases efficiently summarize a document's content and are used in various document processing and retrieval tasks. Several unsupervised techniques and classifiers exist for extracting keyphrases from text documents. Most of these methods operate at a phrase-level and rely on part-of-speech (POS) filters for candidate phrase generation. In addition, they do not directly handle keyphrases of varying lengths. We overcome these modeling shortcomings by addressing keyphrase extraction as a sequential labeling task in this paper. We explore a basic set of features commonly used in NLP tasks as well as predictions from various unsupervised methods to train our taggers. In addition to a more natural modeling for the keyphrase extraction problem, we show that tagging models yield significant performance benefits over existing state-of-the-art extraction methods.