# Why Did You Not Compare With That? Identifying Papers for Use as Baselines

arXiv: https://arxiv.org/abs/2201.08089 · HTML: https://mlsanity.com/paper/2201.08089/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 20 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://mlsanity.com/paper/1904.01608/) (2019)
- [Content-Based Citation Recommendation](https://mlsanity.com/paper/1802.08301/) (2018)
- [A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks](https://mlsanity.com/paper/1903.06464/) (2019)
- [Neural Multi-Task Learning for Citation Function and Provenance](https://mlsanity.com/paper/1811.07351/) (2018)
- [Growth in the number of references in engineering journal papers during the 1972-2013 period](https://mlsanity.com/paper/1306.4223/) (2013)
- [Context Sensitive Article Ranking with Citation Context Analysis](https://mlsanity.com/paper/1511.04946/) (2015)
- [All Fingers are not Equal: Intensity of References in Scientific Articles](https://mlsanity.com/paper/1609.00081/) (2016)

## Built on by (most cited first, 3 of 3)

- [Hierarchical Catalogue Generation for Literature Review: A Benchmark](https://mlsanity.com/paper/2304.03512/) (2023)
- [LEGOBench: Scientific Leaderboard Generation Benchmark](https://mlsanity.com/paper/2401.06233/) (2024)
- [Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach](https://mlsanity.com/paper/2311.03401/) (2023)

## Abstract (arXiv)

We propose the task of automatically identifying papers used as baselines in a scientific article. We frame the problem as a binary classification task where all the references in a paper are to be classified as either baselines or non-baselines. This is a challenging problem due to the numerous ways in which a baseline reference can appear in a paper. We develop a dataset of 2,075 papers from ACL anthology corpus with all their references manually annotated as one of the two classes. We develop a multi-module attention-based neural classifier for the baseline classification task that outperforms four state-of-the-art citation role classification methods when applied to the baseline classification task. We also present an analysis of the errors made by the proposed classifier, eliciting the challenges that make baseline identification a challenging problem.