# A Survey on Neural Topic Models: Methods, Applications, and Challenges

arXiv: https://arxiv.org/abs/2401.15351 · HTML: https://mlsanity.com/paper/2401.15351/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 165 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Builds on (most cited first, 30 of 61)

- [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)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Fact-Checking Complex Claims with Program-Guided Reasoning](https://mlsanity.com/paper/2305.12744/) (2023)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence](https://mlsanity.com/paper/2004.03974/) (2020)
- [Neural Variational Inference for Text Processing](https://mlsanity.com/paper/1511.06038/) (2015)
- [Discovering Discrete Latent Topics with Neural Variational Inference](https://mlsanity.com/paper/1706.00359/) (2017)
- [Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence](https://mlsanity.com/paper/2107.02173/) (2021)
- [Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!](https://mlsanity.com/paper/2004.14914/) (2020)
- [Cross-lingual Contextualized Topic Models with Zero-shot Learning](https://mlsanity.com/paper/2004.07737/) (2020)
- [Topic Modeling with Wasserstein Autoencoders](https://mlsanity.com/paper/1907.12374/) (2019)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [Neural Topic Model via Optimal Transport](https://mlsanity.com/paper/2008.13537/) (2020)
- [Contrastive Learning for Neural Topic Model](https://mlsanity.com/paper/2110.12764/) (2021)
- [Neural Models for Documents with Metadata](https://mlsanity.com/paper/1705.09296/) (2017)
- [Effective Neural Topic Modeling with Embedding Clustering Regularization](https://mlsanity.com/paper/2306.04217/) (2023)
- [Dirichlet Variational Autoencoder](https://mlsanity.com/paper/1901.02739/) (2019)
- [The Dynamic Embedded Topic Model](https://mlsanity.com/paper/1907.05545/) (2019)
- [Topic Modeling with Contextualized Word Representation Clusters](https://mlsanity.com/paper/2010.12626/) (2020)
- [Short Text Topic Modeling Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1904.07695/) (2019)

## Built on by (most cited first, 18 of 18)

- [FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model](https://mlsanity.com/paper/2405.17978/) (2024)
- [Topic Modeling as Multi-Objective Contrastive Optimization](https://mlsanity.com/paper/2402.07577/) (2024)
- [Aspect-Based Summarization with Self-Aspect Retrieval Enhanced Generation](https://mlsanity.com/paper/2504.13054/) (2025)
- [Sparse Autoencoders are Topic Models](https://mlsanity.com/paper/2511.16309/) (2025)
- [Importance-aware Topic Modeling for Discovering Public Transit Risk from Noisy Social Media](https://mlsanity.com/paper/2512.06293/) (2025)
- [Poisson-Process Topic Model for Integrating Knowledge from Pre-trained Language Models](https://mlsanity.com/paper/2503.17809/) (2025)
- [Merging Embedded Topics with Optimal Transport for Online Topic Modeling on Data Streams](https://mlsanity.com/paper/2504.07711/) (2025)
- [E-LDA: Toward Interpretable LDA Topic Models with Strong Guarantees in Logarithmic Parallel Time](https://mlsanity.com/paper/2506.07747/) (2025)
- [Discourse-Aware Scientific Paper Recommendation via QA-Style Summarization and Multi-Level Contrastive Learning](https://mlsanity.com/paper/2511.03330/) (2025)
- [Hierarchical mixtures of Unigram models for short text clustering: The role of Beta-Liouville priors](https://mlsanity.com/paper/2410.21862/) (2024)
- [pytopicgram: A library for data extraction and topic modeling from Telegram channels](https://mlsanity.com/paper/2502.04882/) (2025)
- [Self-Supervised Learning for Neural Topic Models with Variance-Invariance-Covariance Regularization](https://mlsanity.com/paper/2502.09944/) (2025)
- [Automated Bug Report Prioritization in Large Open-Source Projects](https://mlsanity.com/paper/2504.15912/) (2025)
- [Towards an automatic method for generating topical vocabulary test forms for specific reading passages](https://mlsanity.com/paper/2505.18762/) (2025)
- [scE2TM improves single-cell embedding interpretability and reveals cellular perturbation signatures](https://mlsanity.com/paper/2507.08355/) (2025)
- [Mapping Scientific Literature with Large Language Models and Topic Modeling](https://mlsanity.com/paper/2510.16152/) (2025)
- [From the Rock Floor to the Cloud: A Systematic Survey of State-of-the-Art NLP in Battery Life Cycle](https://mlsanity.com/paper/2510.27369/) (2025)
- [Population-level measures of perceived food access reveal barriers beyond geographic proximity](https://mlsanity.com/paper/2609.12132/) (2026)

## Abstract (arXiv)

Topic models have been prevalent for decades to discover latent topics and infer topic proportions of documents in an unsupervised fashion. They have been widely used in various applications like text analysis and context recommendation. Recently, the rise of neural networks has facilitated the emergence of a new research field -- Neural Topic Models (NTMs). Different from conventional topic models, NTMs directly optimize parameters without requiring model-specific derivations. This endows NTMs with better scalability and flexibility, resulting in significant research attention and plentiful new methods and applications. In this paper, we present a comprehensive survey on neural topic models concerning methods, applications, and challenges. Specifically, we systematically organize current NTM methods according to their network structures and introduce the NTMs for various scenarios like short texts and bilingual documents. We also discuss a wide range of popular applications built on NTMs. Finally, we highlight the challenges confronted by NTMs to inspire future research. We accompany this survey with a repository for easier access to the mentioned paper resources: https://github.com/bobxwu/Paper-Neural-Topic-Models.