@inproceedings{SisLab4216,
       booktitle = {The 25th International Conference on Pattern Recognition},
           title = {Learning Neural Textual Representations for Citation Recommendation},
          author = {Thanh Binh Kieu and Inigo Jauregi Unanue and Bao Son Pham and Xuan Hieu Phan and Massimo Piccardi},
            year = {2020},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4216/},
        abstract = {With the rapid growth of the scientific literature, manually selecting appropriate citations for a paper is becoming increasingly challenging and time-consuming. While several approaches for automated citation recommendation have been proposed in the recent years, effective document representations for citation recommendation are still elusive to a large extent. For this reason, in this paper we propose a novel approach to citation recommendation which leverages a deep sequential representation of the documents (Sentence-BERT) cascaded with Siamese and triplet networks in a submodular scoring function. To the best of our knowledge, this is the first approach to combine deep representations and submodular selection for a task of citation recommendation. Experiments have been carried out using a popular benchmark dataset - the ACL Anthology Network corpus - and evaluated against baselines and a state-of-the-art approach using metrics such as the MRR and F1-at-k score. The results show that the proposed approach has been able to outperform all the compared approaches in every measured metric.}
}