Keyphrase Generation for Scientific Document Retrieval

Florian Boudin, Ygor Gallina, Akiko Aizawa


Abstract
Sequence-to-sequence models have lead to significant progress in keyphrase generation, but it remains unknown whether they are reliable enough to be beneficial for document retrieval. This study provides empirical evidence that such models can significantly improve retrieval performance, and introduces a new extrinsic evaluation framework that allows for a better understanding of the limitations of keyphrase generation models. Using this framework, we point out and discuss the difficulties encountered with supplementing documents with -not present in text- keyphrases, and generalizing models across domains. Our code is available at https://github.com/boudinfl/ir-using-kg
Anthology ID:
2020.acl-main.105
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1118–1126
Language:
URL:
https://aclanthology.org/2020.acl-main.105
DOI:
10.18653/v1/2020.acl-main.105
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.105.pdf
Video:
 http://slideslive.com/38928736
Code
 boudinfl/ir-using-kg
Data
KP20k