# The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study

arXiv: https://arxiv.org/abs/2302.03341 · HTML: https://mlsanity.com/paper/2302.03341/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 62 references that are in the map.
- First posted on arXiv 7 Feb 2023.

## 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)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018)
- [AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification](https://mlsanity.com/paper/1811.01727/) (2018)
- [The Emerging Trends of Multi-Label Learning](https://mlsanity.com/paper/2011.11197/) (2020)
- [DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification](https://mlsanity.com/paper/1609.02521/) (2016)
- [Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2110.00685/) (2021)
- [Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification](https://mlsanity.com/paper/1904.08249/) (2019)
- [LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification](https://mlsanity.com/paper/2101.03305/) (2021)
- [PECOS: Prediction for Enormous and Correlated Output Spaces](https://mlsanity.com/paper/2010.05878/) (2020)
- [A Web-scale system for scientific knowledge exploration](https://mlsanity.com/paper/1805.12216/) (2018)
- [ECLARE: Extreme Classification with Label Graph Correlations](https://mlsanity.com/paper/2108.00261/) (2021)
- [DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents](https://mlsanity.com/paper/2111.06685/) (2021)
- [DECAF: Deep Extreme Classification with Label Features](https://mlsanity.com/paper/2108.00368/) (2021)
- [Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2007.02439/) (2020)
- [MATCH: Metadata-Aware Text Classification in A Large Hierarchy](https://mlsanity.com/paper/2102.07349/) (2021)
- [OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge Services](https://mlsanity.com/paper/2103.02410/) (2021)
- [Hierarchical Metadata-Aware Document Categorization under Weak Supervision](https://mlsanity.com/paper/2010.13556/) (2020)
- [A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations](https://mlsanity.com/paper/1704.05150/) (2017)
- [Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification](https://mlsanity.com/paper/2202.05932/) (2022)
- [MotifClass: Weakly Supervised Text Classification with Higher-order Metadata Information](https://mlsanity.com/paper/2111.04022/) (2021)
- [Scientific Prizes and the Extraordinary Growth of Scientific Topics](https://mlsanity.com/paper/2012.09269/) (2020)
- [Is preprint the future of science? A thirty year journey of online preprint services](https://mlsanity.com/paper/2102.09066/) (2021)

## Built on by (most cited first, 17 of 17)

- [Large Language Models on Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2312.02783/) (2023)
- [Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs](https://mlsanity.com/paper/2404.07103/) (2024)
- [A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery](https://mlsanity.com/paper/2406.10833/) (2024)
- [A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications](https://mlsanity.com/paper/2404.14809/) (2024)
- [Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding](https://mlsanity.com/paper/2305.14232/) (2023)
- [Chain-of-Factors Paper-Reviewer Matching](https://mlsanity.com/paper/2310.14483/) (2023)
- [Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking](https://mlsanity.com/paper/2505.21815/) (2025)
- [LinkGPT: Teaching Large Language Models To Predict Missing Links](https://mlsanity.com/paper/2406.04640/) (2024)
- [Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training](https://mlsanity.com/paper/2306.07193/) (2023)
- [A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion](https://mlsanity.com/paper/2402.13405/) (2024)
- [Investigating Instruction Tuning Large Language Models on Graphs](https://mlsanity.com/paper/2408.05457/) (2024)
- [Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers](https://mlsanity.com/paper/2306.14003/) (2023)
- [Parameter-Efficient Tuning Large Language Models for Graph Representation Learning](https://mlsanity.com/paper/2404.18271/) (2024)
- [Hierarchical Compression of Text-Rich Graphs via Large Language Models](https://mlsanity.com/paper/2406.11884/) (2024)
- [A Scalable Pretraining Framework for Link Prediction with Efficient Adaptation](https://mlsanity.com/paper/2508.04645/) (2025)
- [SciImpact: A Multi-Dimensional, Multi-Field Benchmark for Scientific Impact Prediction](https://mlsanity.com/paper/2604.17141/) (2026)
- [Reliable Academic Conference Question Answering: A Study Based on Large Language Model](https://mlsanity.com/paper/2310.13028/) (2023)

## Abstract (arXiv)

Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature. Scientific literature tagging is beyond a pure multi-label text classification task because papers on the Web are prevalently accompanied by metadata information such as venues, authors, and references, which may serve as additional signals to infer relevant tags. Although there have been studies making use of metadata in academic paper classification, their focus is often restricted to one or two scientific fields (e.g., computer science and biomedicine) and to one specific model. In this work, we systematically study the effect of metadata on scientific literature tagging across 19 fields. We select three representative multi-label classifiers (i.e., a bag-of-words model, a sequence-based model, and a pre-trained language model) and explore their performance change in scientific literature tagging when metadata are fed to the classifiers as additional features. We observe some ubiquitous patterns of metadata's effects across all fields (e.g., venues are consistently beneficial to paper tagging in almost all cases), as well as some unique patterns in fields other than computer science and biomedicine, which are not explored in previous studies.