# Inline Citation Classification using Peripheral Context and Time-evolving Augmentation

## TL;DR

This paper introduces a new dataset and a Transformer-based model for inline citation classification that leverages peripheral context and domain knowledge, achieving state-of-the-art results.

## Contribution

The study presents 3Cext, a novel dataset with discourse information, and PeriCite, a deep neural network that fuses peripheral sentences and domain knowledge for improved citation classification.

## Key findings

- PeriCite outperforms baselines with +0.09 F1 on 3Cext
- The dataset includes discourse and domain context for citation analysis
- Extensive ablations validate the effectiveness of context and knowledge fusion

## Abstract

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.