# S2ORC: The Semantic Scholar Open Research Corpus

arXiv: https://arxiv.org/abs/1911.02782 · HTML: https://mlsanity.com/paper/1911.02782/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2019.

## Built on by (most cited first, 10 of 10)

- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [Generative Representational Instruction Tuning](https://mlsanity.com/paper/2402.09906/) (2024)
- [Learning Rich Representation of Keyphrases from Text](https://mlsanity.com/paper/2112.08547/) (2021)
- [Enhancing Keyphrase Extraction from Long Scientific Documents using Graph Embeddings](https://mlsanity.com/paper/2305.09316/) (2023)
- [Lacuna: A Research Map for Machine Learning](https://mlsanity.com/paper/2606.26246/) (2026)
- [SCALAR: Scientific Citation-based Live Assessment of Long-context Academic Reasoning](https://mlsanity.com/paper/2502.13753/) (2025)
- [MasonNLP+ at SemEval-2023 Task 8: Extracting Medical Questions, Experiences and Claims from Social Media using Knowledge-Augmented Pre-trained Language Models](https://mlsanity.com/paper/2304.13875/) (2023)
- [Lit2Vec: A Reproducible Workflow for Building a Legally Screened Chemistry Corpus from S2ORC for Downstream Retrieval and Text Mining](https://mlsanity.com/paper/2604.12498/) (2026)
- [Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings](https://mlsanity.com/paper/2606.10716/) (2026)
- [Backdoor Learning in Language Models and Vision-Language Models](https://mlsanity.com/paper/2608.18095/) (2026)

## Abstract (arXiv)

We introduce S2ORC, a large corpus of 81.1M English-language academic papers spanning many academic disciplines. The corpus consists of rich metadata, paper abstracts, resolved bibliographic references, as well as structured full text for 8.1M open access papers. Full text is annotated with automatically-detected inline mentions of citations, figures, and tables, each linked to their corresponding paper objects. In S2ORC, we aggregate papers from hundreds of academic publishers and digital archives into a unified source, and create the largest publicly-available collection of machine-readable academic text to date. We hope this resource will facilitate research and development of tools and tasks for text mining over academic text.