# Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation

arXiv: https://arxiv.org/abs/2001.08687 · HTML: https://mlsanity.com/paper/2001.08687/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [Towards Generating Citation Sentences for Multiple References with Intent Control](https://mlsanity.com/paper/2112.01332/) (2021)
- [When Large Language Models Meet Citation: A Survey](https://mlsanity.com/paper/2309.09727/) (2023)
- [Transformers and Language Models in Form Understanding: A Comprehensive Review of Scanned Document Analysis](https://mlsanity.com/paper/2403.04080/) (2024)

## Abstract (arXiv)

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.