# Graphine: A Dataset for Graph-aware Terminology Definition Generation

arXiv: https://arxiv.org/abs/2109.04018 · HTML: https://mlsanity.com/paper/2109.04018/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 67 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [Hyperbolic Neural Networks](https://mlsanity.com/paper/1805.09112/) (2018)
- [Hyperbolic Graph Convolutional Neural Networks](https://mlsanity.com/paper/1910.12933/) (2019)
- [Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge](https://mlsanity.com/paper/1609.06647/) (2016)
- [Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation](https://mlsanity.com/paper/1903.10122/) (2019)
- [Attribute2Image: Conditional Image Generation from Visual Attributes](https://mlsanity.com/paper/1512.00570/) (2015)
- [Graph Transformer for Graph-to-Sequence Learning](https://mlsanity.com/paper/1911.07470/) (2019)
- [A Graph-to-Sequence Model for AMR-to-Text Generation](https://mlsanity.com/paper/1805.02473/) (2018)
- [Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints](https://mlsanity.com/paper/2005.00969/) (2020)
- [GPT-too: A language-model-first approach for AMR-to-text generation](https://mlsanity.com/paper/2005.09123/) (2020)
- [CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training](https://mlsanity.com/paper/2006.04702/) (2020)
- [Unsupervised Text Generation by Learning from Search](https://mlsanity.com/paper/2007.08557/) (2020)
- [Modeling Graph Structure in Transformer for Better AMR-to-Text Generation](https://mlsanity.com/paper/1909.00136/) (2019)
- [ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation](https://mlsanity.com/paper/2101.08382/) (2021)
- [Deep Graph Convolutional Encoders for Structured Data to Text Generation](https://mlsanity.com/paper/1810.09995/) (2018)
- [Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents](https://mlsanity.com/paper/1910.03487/) (2019)
- [Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks](https://mlsanity.com/paper/1910.14326/) (2019)
- [A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency](https://mlsanity.com/paper/1911.01678/) (2019)
- [Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors](https://mlsanity.com/paper/1906.05939/) (2019)

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

- [Understanding Jargon: Combining Extraction and Generation for Definition Modeling](https://mlsanity.com/paper/2111.07267/) (2021)
- [CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts](https://mlsanity.com/paper/2408.09070/) (2024)
- [Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds](https://mlsanity.com/paper/2205.01845/) (2022)
- [Graph-in-Graph Network for Automatic Gene Ontology Description Generation](https://mlsanity.com/paper/2206.05311/) (2022)
- [Multilingual Definition Modeling](https://mlsanity.com/paper/2506.01489/) (2025)
- [KnowCoder-V2: Deep Knowledge Analysis](https://mlsanity.com/paper/2506.06881/) (2025)
- [MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks](https://mlsanity.com/paper/2210.07488/) (2022)
- [JEBS: A Fine-grained Biomedical Lexical Simplification Task](https://mlsanity.com/paper/2506.12898/) (2025)
- [SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models](https://mlsanity.com/paper/2605.19357/) (2026)
- [Domain-Specific Jargon in Large Language Models: A Comparative Analysis between General-Purpose and Specialist Models](https://mlsanity.com/paper/2609.13556/) (2026)

## Abstract (arXiv)

Precisely defining the terminology is the first step in scientific communication. Developing neural text generation models for definition generation can circumvent the labor-intensity curation, further accelerating scientific discovery. Unfortunately, the lack of large-scale terminology definition dataset hinders the process toward definition generation. In this paper, we present a large-scale terminology definition dataset Graphine covering 2,010,648 terminology definition pairs, spanning 227 biomedical subdisciplines. Terminologies in each subdiscipline further form a directed acyclic graph, opening up new avenues for developing graph-aware text generation models. We then proposed a novel graph-aware definition generation model Graphex that integrates transformer with graph neural network. Our model outperforms existing text generation models by exploiting the graph structure of terminologies. We further demonstrated how Graphine can be used to evaluate pretrained language models, compare graph representation learning methods and predict sentence granularity. We envision Graphine to be a unique resource for definition generation and many other NLP tasks in biomedicine.