  Citation plays a pivotal role in determining the associations among research
articles. It portrays essential information in indicative, supportive, or
contrastive studies. The task of inline citation classification aids in
extrapolating these relationships; However, existing studies are still immature
and demand further scrutiny. Current datasets and methods used for inline
citation classification only use citation-marked sentences constraining the
model to turn a blind eye to domain knowledge and neighboring contextual
sentences. In this paper, we propose a new dataset, named 3Cext, which along
with the cited sentences, provides discourse information using the vicinal
sentences to analyze the contrasting and entailing relationships as well as
domain information. We propose PeriCite, a Transformer-based deep neural
network that fuses peripheral sentences and domain knowledge. Our model
achieves the state-of-the-art on the 3Cext dataset by +0.09 F1 against the best
baseline. We conduct extensive ablations to analyze the efficacy of the
proposed dataset and model fusion methods.
