Keyphrase Generation for Scientific Document Retrieval

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

arXiv commentAccepted at ACL 2020

References in corpus (6)

• Neural Machine Translation by Jointly Learning to Align and Translate
• Sequence to Sequence Learning with Neural Networks
• Document Expansion by Query Prediction
• Coherent Keyphrase Extraction via Web Mining
• Critically Examining the "Neural Hype": Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models
• Keyphrase Annotation with Graph Co-Ranking

Cited by in corpus (5)

• Pre-Trained Language Models for Keyphrase Prediction: A Review
• Topological Information Retrieval with Dilation-Invariant Bottleneck Comparative Measures
• Capturing Global Informativeness in Open Domain Keyphrase Extraction
• Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation
• Deep Keyphrase Completion