SOTAVerified

Keyphrase Generation for Scientific Document Retrieval

2021-06-28ACL 2020Code Available1· sign in to hype

Florian Boudin, Ygor Gallina, Akiko Aizawa

Code Available — Be the first to reproduce this paper.

Reproduce

Code

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

Tasks

Reproductions