  Citing pertinent literature is pivotal to writing and reviewing a scientific
document. Existing techniques mainly focus on the local context or the global
context for recommending citations but fail to consider the actual human
citation behaviour. We propose SymTax, a three-stage recommendation
architecture that considers both the local and the global context, and
additionally the taxonomical representations of query-candidate tuples and the
Symbiosis prevailing amongst them. SymTax learns to embed the infused
taxonomies in the hyperbolic space and uses hyperbolic separation as a latent
feature to compute query-candidate similarity. We build a novel and large
dataset ArSyTa containing 8.27 million citation contexts and describe the
creation process in detail. We conduct extensive experiments and ablation
studies to demonstrate the effectiveness and design choice of each module in
our framework. Also, combinatorial analysis from our experiments shed light on
the choice of language models (LMs) and fusion embedding, and the inclusion of
section heading as a signal. Our proposed module that captures the symbiotic
relationship solely leads to performance gains of 26.66% and 39.25% in Recall@5
w.r.t. SOTA on ACL-200 and RefSeer datasets, respectively. The complete
framework yields a gain of 22.56% in Recall@5 wrt SOTA on our proposed dataset.
The code and dataset are available at https://github.com/goyalkaraniit/SymTax
