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Keyphrase Generation for Scientific Document Retrieval

Abstract

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 https://github.com/boudinfl/ir-using-kg
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Dates and versions

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

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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⟩
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