Márton Kardos, Jan Kostkan, Kenneth Enevoldsen, Arnault-Quentin Vermillet, Kristoffer Nielbo, Roberta Rocca
| Challenge: | Recent efforts to incorporate contextual representations into topic models have been shown to outperform classical topic models. |
| Approach: | They propose a theory-driven topic modeling approach that decomposes contextualized document embeddings into a Python package that implements S3 and all contextual baselines. |
| Outcome: | The proposed model is 4.5x faster than the BERTopic model and provides diverse and highly coherent topics with no preprocessing. |
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| Challenge: | Current neural approaches to topic modeling have not been able to solve all of the problems. |
| Approach: | They propose a topic modeling approach that uses document contextual token embeddings to find topics and find topic spans within documents. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on a comprehensive set of topic model evaluation metrics. |
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence (2021.acl-short)
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| Challenge: | Recent neural topic models extract words from documents, but they are not coherent . coherence is crucial for topic models, but many use bag-of-words document representations as input . pre-trained language models are becoming ubiquitous in natural language processing . |
| Approach: | They combine contextualized representations with neural topic models to produce more coherent topics . they say that future improvements in language models will translate into better topic models . |
| Outcome: | The proposed approach produces more meaningful and coherent topics than bag-of-words models and recent neural models. |
CAST: Corpus-Aware Self-similarity Enhanced Topic modelling (2025.naacl-long)
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Yanan Ma, Chenghao Xiao, Chenhan Yuan, Sabine N Van Der Veer, Lamiece Hassan, Chenghua Lin, Goran Nenadic
| Challenge: | Existing topic modelling methods encode contextual information of documents while ignoring contextual details of candidate centroid words. Existing methods are limited by the contextualization gap. |
| Approach: | They propose a topic modelling method that builds upon candidate centroid word embeddings contextualized on the dataset and a self-similarity-based method to filter out less meaningful tokens. |
| Outcome: | The proposed method significantly enhances the coherence and diversity of generated topics, and handles noisy data, outperforming strong baselines. |
Semantic Component Analysis: Introducing Multi-Topic Distributions to Clustering-Based Topic Modeling (2025.findings-emnlp)
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| Challenge: | Existing methods for topic modeling fail to scale to large datasets or assume one topic per document. |
| Approach: | They propose a topic modeling technique that discovers multiple topics per sample . they evaluate SCA on Twitter datasets in English, Hausa and Chinese . |
| Outcome: | The proposed technique outperforms the LLM-based TopicGPT on Twitter datasets with similar compute budgets. |
Topics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural Networks (2024.lrec-main)
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| Challenge: | Topic models aim to reveal latent structures within corpus of text through term-frequency statistics over bag-of-words representations. |
| Approach: | They propose to use bimodal vector representations of entities to extract latent representations from large language models and graph neural networks trained on symbolic relations to derive the most salient aspects of these conceptual units. |
| Outcome: | The proposed approach is better suited to working with entities than state-of-the-art models. |
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics (2022.naacl-main)
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| Challenge: | Recent work incorporates pre-trained word embeddings into Neural Topic Models (NTMs), generating highly coherent topics. |
| Approach: | They conduct thorough experiments to investigate whether embeddings directly with an appropriate word selection method can generate more coherent and diverse topics than NTMs. |
| Outcome: | The proposed model generates more coherent and diverse topics than traditional NTMs, achieving higher efficiency and simplicity. |
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge (2022.acl-long)
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| Challenge: | Recent studies have shown that using external knowledge such as pre-trained word embeddings or pre-train language models only achieved limited performance improvements but with huge computational overhead. |
| Approach: | They propose to incorporate external knowledge into neural topic modeling by pre-trained word embeddings (PWEs) or pre-train language models (PLMs) they propose to fine-tune the neural topic model on the target dataset and reduce the huge size of training data. |
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HiCOT: Improving Neural Topic Models via Optimal Transport and Contrastive Learning (2025.findings-acl)
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| Challenge: | Recent advances in neural topic models (NTMs) have improved topic quality but still face challenges: weak document-topic alignment, high inference costs due to large pretrained language models, and limited modeling of hierarchical topic structures. |
| Approach: | They propose a framework that integrates hierarchical clustering and contrastive learning to refine document-topic relationships using compact PLM-based embeddings. |
| Outcome: | The proposed framework improves topic coherence, topic performance, representation quality and computational efficiency over existing NTMs. |
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces (2021.naacl-main)
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| Challenge: | Recent work has used pre-trained word embeddings to address data sparsity in short-text or small document collections. |
| Approach: | They propose a neural topic modeling framework using multi-view embedding spaces to improve topic quality and deal with polysemy. |
| Outcome: | The proposed framework improves topic quality and deal with polysemy. |
Dynamic Structured Neural Topic Model with Self-Attention Mechanism (2023.findings-acl)
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| Challenge: | Recent topic models that capture the time-series evolution of topics assume that topics evolve independently without interaction. |
| Approach: | They propose a dynamic structured neural topic model which captures topic dependencies while capturing their dependencies. |
| Outcome: | The proposed model outperforms a prior dynamic embedded topic model regarding perplexity and coherence while maintaining sufficient diversity across topics. |