# Incidental or influential? - Challenges in automatically detecting citation importance using publication full texts

arXiv: https://arxiv.org/abs/1707.04207 · HTML: https://mlsanity.com/paper/1707.04207/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2017.

## Built on by (most cited first, 2 of 2)

- [BibAgent: An Agentic Framework for Traceable Miscitation Detection in Scientific Literature](https://mlsanity.com/paper/2601.16993/) (2026)
- [PST-Bench: Tracing and Benchmarking the Source of Publications](https://mlsanity.com/paper/2402.16009/) (2024)

## Abstract (arXiv)

This work looks in depth at several studies that have attempted to automate the process of citation importance classification based on the publications full text. We analyse a range of features that have been previously used in this task. Our experimental results confirm that the number of in text references are highly predictive of influence. Contrary to the work of Valenzuela et al. we find abstract similarity one of the most predictive features. Overall, we show that many of the features previously described in literature are not particularly predictive. Consequently, we discuss challenges and potential improvements in the classification pipeline, provide a critical review of the performance of individual features and address the importance of constructing a large scale gold standard reference dataset.