Work

Keyphrase Generation for Scientific Document Retrieval

Year: 2020

Type: conference-paper

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 textkeyphrases, and generalizing models across domains.Our code is available at https:// github.com/boudinfl/ir-using-kg.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/2020.acl-main.105

Authors: Florian Boudin, Ygor Gallina, Akiko Aizawa

Institutions: Laboratoire des Sciences du Numérique de Nantes, Université de Nantes, Nantes Université, National Institute of Informatics

Language: English

Topic: Advanced Text Analysis Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Agence Nationale de la Recherche

Awards: G3010527862, Indexing scientific literature through semantic expansion