Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-based Reranking
This work addresses the problem of efficient and accurate citation recommendation for researchers, though it is incremental as it builds on existing prefetching and reranking approaches.
The paper tackled local citation recommendation by proposing a hierarchical attention network for prefetching and a SciBERT-based reranker, achieving state-of-the-art performance on multiple datasets with high prefetch recall.
The goal of local citation recommendation is to recommend a missing reference from the local citation context and optionally also from the global context. To balance the tradeoff between speed and accuracy of citation recommendation in the context of a large-scale paper database, a viable approach is to first prefetch a limited number of relevant documents using efficient ranking methods and then to perform a fine-grained reranking using more sophisticated models. In that vein, BM25 has been found to be a tough-to-beat approach to prefetching, which is why recent work has focused mainly on the reranking step. Even so, we explore prefetching with nearest neighbor search among text embeddings constructed by a hierarchical attention network. When coupled with a SciBERT reranker fine-tuned on local citation recommendation tasks, our hierarchical Attention encoder (HAtten) achieves high prefetch recall for a given number of candidates to be reranked. Consequently, our reranker requires fewer prefetch candidates to rerank, yet still achieves state-of-the-art performance on various local citation recommendation datasets such as ACL-200, FullTextPeerRead, RefSeer, and arXiv.