Keyphrase Generation for Scientific Document Retrieval - Archive ouverte HAL Access content directly
Conference Papers Year :

Keyphrase Generation for Scientific Document Retrieval


Sequence-to-sequence models have lead to significant progress in keyphrase generation, butit remains unknown whether they are reli-able enough to be beneficial for document re-trieval.This study provides empirical evi-dence that such models can significantly improve retrieval performance, and introducesa new extrinsic evaluation framework that al-lows for a better understanding of the limi-tations of keyphrase generation models. Using this framework, we point out and dis-cuss the difficulties encountered with supplementing documents with –not present in text– keyphrases, and generalizing models acrossdomains. Our code is available at
Fichier principal
Vignette du fichier
nkg4ir.pdf (130.89 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-02556086 , version 1 (27-04-2020)
hal-02556086 , version 2 (13-05-2020)



Florian Boudin, Ygor Gallina, Akiko Aa Aizawa. Keyphrase Generation for Scientific Document Retrieval. The 58th Annual Meeting of the Association for Computational Linguistics (ACL), Jul 2020, Online, United States. ⟨10.18653/v1/2020.acl-main.105⟩. ⟨hal-02556086v2⟩
176 View
251 Download



Gmail Facebook Twitter LinkedIn More