Paragraph-level Citation Recommendation based on Topic Sentences as Queries

Citation recommendation (CR) models may help authors find relevant articles at various stages of the paper writing process. Most research has dealt with either global CR, which produces general recommendations suitable for the initial writing stage, or local CR, which produces specific recommendations more fitting for the final writing stages. We propose the task of paragraph-level CR as a middle ground between the two approaches, where the paragraph's topic sentence is taken as input and recommendations for citing within the paragraph are produced at the output. We propose a model for this task, fine-tune it using the quadruplet loss on the dataset of ACL papers, and show improvements over the baselines.

Large-scale Evaluation of Transformer-based Article Encoders on the Task of Citation Recommendation

Recently introduced transformer-based article encoders (TAEs) designed to produce similar vector representations for mutually related scientific articles have demonstrated strong performance on benchmark datasets for scientific article recommendation. However, the existing benchmark datasets are predominantly focused on single domains and, in some cases, contain easy negatives in small candidate pools. Evaluating representations on such benchmarks might obscure the realistic performance of TAEs in setups with thousands of articles in candidate pools. In this work, we evaluate TAEs on large benchmarks with more challenging candidate pools. We compare the performance of TAEs with a lexical retrieval baseline model BM25 on the task of citation recommendation, where the model produces a list of recommendations for citing in a given input article. We find out that BM25 is still very competitive with the state-of-the-art neural retrievers, a finding which is surprising given the strong performance of TAEs on small benchmarks. As a remedy for the limitations of the existing benchmarks, we propose a new benchmark dataset for evaluating scientific article representations: Multi-Domain Citation Recommendation dataset (MDCR), which covers different scientific fields and contains challenging candidate pools.