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Communication Dans Un Congrès Année : 2020

Keyphrase Generation for Scientific Document Retrieval

Résumé

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 di culties 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.
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Dates et versions

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

Identifiants

  • HAL Id : hal-02556086 , version 1

Citer

Florian Boudin, Ygor Gallina, Akiko Aa Aizawa. Keyphrase Generation for Scientific Document Retrieval. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), Jul 2020, Seattle, Washington, United States. ⟨hal-02556086v1⟩
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