Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness

Florian Boudin, Ygor Gallina


Abstract
Neural keyphrase generation models have recently attracted much interest due to their ability to output absent keyphrases, that is, keyphrases that do not appear in the source text. In this paper, we discuss the usefulness of absent keyphrases from an Information Retrieval (IR) perspective, and show that the commonly drawn distinction between present and absent keyphrases is not made explicit enough. We introduce a finer-grained categorization scheme that sheds more light on the impact of absent keyphrases on scientific document retrieval. Under this scheme, we find that only a fraction (around 20%) of the words that make up keyphrases actually serves as document expansion, but that this small fraction of words is behind much of the gains observed in retrieval effectiveness. We also discuss how the proposed scheme can offer a new angle to evaluate the output of neural keyphrase generation models.
Anthology ID:
2021.naacl-main.330
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4185–4193
Language:
URL:
https://aclanthology.org/2021.naacl-main.330
DOI:
10.18653/v1/2021.naacl-main.330
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.330.pdf
Data
KP20k