relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4216/
title: Learning Neural Textual Representations for Citation Recommendation
creator: Kieu, Thanh Binh
creator: Unanue, Inigo Jauregi
creator: Pham, Bao Son
creator: Phan, Xuan Hieu
creator: Piccardi, Massimo
subject: Information Technology (IT)
subject: ISI/Scopus indexed conference
description: 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.
date: 2020
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Kieu, Thanh Binh and Unanue, Inigo Jauregi and Pham, Bao Son and Phan, Xuan Hieu and Piccardi, Massimo  (2020) Learning Neural Textual Representations for Citation Recommendation.  In: The 25th International Conference on Pattern Recognition, Milan, Italy.     
relation: https://www.micc.unifi.it/icpr2020/