# unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network

arXiv: https://arxiv.org/abs/2303.14957 · HTML: https://mlsanity.com/paper/2303.14957/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [Content-Based Citation Recommendation](https://mlsanity.com/paper/1802.08301/) (2018)
- [Citation Recommendation: Approaches and Datasets](https://mlsanity.com/paper/2002.06961/) (2020)
- [SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation](https://mlsanity.com/paper/2110.10774/) (2021)
- [CitationIE: Leveraging the Citation Graph for Scientific Information Extraction](https://mlsanity.com/paper/2106.01560/) (2021)
- [CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision](https://mlsanity.com/paper/2205.06207/) (2022)
- [SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions](https://mlsanity.com/paper/2202.09695/) (2022)
- [Cross-Lingual Citations in English Papers: A Large-Scale Analysis of Prevalence, Usage, and Impact](https://mlsanity.com/paper/2111.05097/) (2021)
- [Comparison of metadata with relevance for bibliometrics between Microsoft Academic Graph and OpenAlex until 2020](https://mlsanity.com/paper/2206.14168/) (2022)

## Built on by (most cited first, 24 of 24)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [The ACL OCL Corpus: Advancing Open Science in Computational Linguistics](https://mlsanity.com/paper/2305.14996/) (2023)
- [LitLLMs, LLMs for Literature Review: Are we there yet?](https://mlsanity.com/paper/2412.15249/) (2024)
- [SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples](https://mlsanity.com/paper/2308.03671/) (2023)
- [ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models](https://mlsanity.com/paper/2410.22360/) (2024)
- [HDT: Hierarchical Document Transformer](https://mlsanity.com/paper/2407.08330/) (2024)
- [Expert Preference-based Evaluation of Automated Related Work Generation](https://mlsanity.com/paper/2508.07955/) (2025)
- [What Should I Cite? A RAG Benchmark for Academic Citation Prediction](https://mlsanity.com/paper/2601.14949/) (2026)
- [Unsupervised Domain Adaptation for Keyphrase Generation using Citation Contexts](https://mlsanity.com/paper/2409.13266/) (2024)
- [Hierarchical Tree-structured Knowledge Graph For Academic Insight Survey](https://mlsanity.com/paper/2402.04854/) (2024)
- [Fine-Tuning Language Models for Scientific Writing Support](https://mlsanity.com/paper/2306.10974/) (2023)
- [SciEvo: A 2 Million, 30-Year Cross-disciplinary Dataset for Temporal Scientometric Analysis](https://mlsanity.com/paper/2410.09510/) (2024)
- [TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation Learning](https://mlsanity.com/paper/2411.15458/) (2024)
- [SciGA: A Comprehensive Dataset for Designing Graphical Abstracts in Academic Papers](https://mlsanity.com/paper/2507.02212/) (2025)
- [The Appeal and Reality of Recycling LoRAs with Adaptive Merging](https://mlsanity.com/paper/2602.12323/) (2026)
- [Object Recognition from Scientific Document based on Compartment Refinement Framework](https://mlsanity.com/paper/2312.09038/) (2023)
- [HyperPIE: Hyperparameter Information Extraction from Scientific Publications](https://mlsanity.com/paper/2312.10638/) (2023)
- [SQuAI: Scientific Question-Answering with Multi-Agent Retrieval-Augmented Generation](https://mlsanity.com/paper/2510.15682/) (2025)
- [SciLaD: A Large-Scale, Transparent, Reproducible Dataset for Natural Scientific Language Processing](https://mlsanity.com/paper/2512.11192/) (2025)
- [Do We Need Bigger Models for Science? Task-Aware Retrieval with Small Language Models](https://mlsanity.com/paper/2604.01965/) (2026)
- [ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation](https://mlsanity.com/paper/2505.24219/) (2025)
- [ResearchArcade: Graph Interface for Academic Tasks](https://mlsanity.com/paper/2511.22036/) (2025)
- [LitBench: A Graph-Centric Large Language Model Benchmarking Tool For Literature Tasks](https://mlsanity.com/paper/2603.00051/) (2026)
- [MUSE: A Full-Text Cross-Domain Knowledge Base of Scientific Problems, Solutions, and Rationales](https://mlsanity.com/paper/2608.10974/) (2026)

## Abstract (arXiv)

Large-scale data sets on scholarly publications are the basis for a variety of bibliometric analyses and natural language processing (NLP) applications. Especially data sets derived from publication's full-text have recently gained attention. While several such data sets already exist, we see key shortcomings in terms of their domain and time coverage, citation network completeness, and representation of full-text content. To address these points, we propose a new version of the data set unarXive. We base our data processing pipeline and output format on two existing data sets, and improve on each of them. Our resulting data set comprises 1.9 M publications spanning multiple disciplines and 32 years. It furthermore has a more complete citation network than its predecessors and retains a richer representation of document structure as well as non-textual publication content such as mathematical notation. In addition to the data set, we provide ready-to-use training/test data for citation recommendation and IMRaD classification. All data and source code is publicly available at https://github.com/IllDepence/unarXive.