Research Area:  Machine Learning

Abstract:

Keyphrase extraction models are usually evaluated under different, not directly comparable, experimental setups. As a result, it remains unclear how well proposed models actually perform, and how they compare to each other. In this work, we address this issue by presenting a systematic large-scale analysis of state-of-the-art keyphrase extraction models involving multiple benchmark datasets from various sources and domains. Our main results reveal that state-of-the-art models are in fact still challenged by simple baselines on some datasets. We also present new insights about the impact of using author- or reader-assigned keyphrases as a proxy for gold standard, and give recommendations for strong baselines and reliable benchmark datasets.

Keywords:  

Author(s) Name:  Ygor Gallina , Florian Boudin , Béatrice Daille

Publisher name:  ACM

DOI:  10.1145/3383583.3398517

Volume Information:  

Paper Link:   https://dl.acm.org/doi/abs/10.1145/3383583.3398517