# How Document Pre-processing affects Keyphrase Extraction Performance

arXiv: https://arxiv.org/abs/1610.07809 · HTML: https://mlsanity.com/paper/1610.07809/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2016.

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

- [Unsupervised Keyphrase Extraction via Interpretable Neural Networks](https://mlsanity.com/paper/2203.07640/) (2022)

## Abstract (arXiv)

The SemEval-2010 benchmark dataset has brought renewed attention to the task of automatic keyphrase extraction. This dataset is made up of scientific articles that were automatically converted from PDF format to plain text and thus require careful preprocessing so that irrevelant spans of text do not negatively affect keyphrase extraction performance. In previous work, a wide range of document preprocessing techniques were described but their impact on the overall performance of keyphrase extraction models is still unexplored. Here, we re-assess the performance of several keyphrase extraction models and measure their robustness against increasingly sophisticated levels of document preprocessing.