Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation
This work addresses the need for efficient citation recommendation to aid authors in scientific writing, though it is incremental as it adapts existing methods to the scientific domain.
The authors tackled the problem of citation recommendation for scientific literature by developing a two-stage ranking approach combining bag-of-words retrieval and BERT re-scoring, achieving the best-reported results on three scientific collections.
Citation recommendation systems for the scientific literature, to help authors find papers that should be cited, have the potential to speed up discoveries and uncover new routes for scientific exploration. We treat this task as a ranking problem, which we tackle with a two-stage approach: candidate generation followed by re-ranking. Within this framework, we adapt to the scientific domain a proven combination based on "bag of words" retrieval followed by re-scoring with a BERT model. We experimentally show the effects of domain adaptation, both in terms of pretraining on in-domain data and exploiting in-domain vocabulary. In addition, we introduce a novel navigation-based document expansion strategy to enrich the candidate documents processed by our neural models. On three different collections from different scientific disciplines, we achieve the best-reported results in the citation recommendation task.