Citation Recommendation on Scholarly Legal Articles
Doğukan Arslan Saadet Sena Erdoğan Gülşen Eryiğit
Citation recommendation is the task of finding appropriate citations based on
a given piece of text. The proposed datasets for this task consist mainly of
several scientific fields, lacking some core ones, such as law. Furthermore,
citation recommendation is used within the legal domain to identify supporting
arguments, utilizing non-scholarly legal articles. In order to alleviate the
limitations of existing studies, we gather the first scholarly legal dataset
for the task of citation recommendation. Also, we conduct experiments with
state-of-the-art models and compare their performance on this dataset. The
study suggests that, while BM25 is a strong benchmark for the legal citation
recommendation task, the most effective method involves implementing a two-step
process that entails pre-fetching with BM25+, followed by re-ranking with
SciNCL, which enhances the performance of the baseline from 0.26 to 0.30
MAP@10. Moreover, fine-tuning leads to considerable performance increases in
pre-trained models, which shows the importance of including legal articles in
the training data of these models.