Added citation boxes to all models in docs

Author: x-tabdeveloping

docs/FASTopic.md

@@ -25,7 +25,7 @@ doc_topic_matrix = model.fit_transform(documents)
 model.print_topics()
 ```
 
-## References
+## Citation
 
 Please cite the authors of the paper, and  Turftopic when using the FASTopic model:
 

docs/GMM.md

@@ -94,6 +94,26 @@ from sklearn.decomposition import IncrementalPCA
 model = GMM(20, dimensionality_reduction=IncrementalPCA(20))
 ```
 
+## Citation
+
+Please cite Turftopic when using GMM in publications:
+
+```bibtex
+@article{
+  Kardos2025,
+  title = {Turftopic: Topic Modelling with Contextual Representations from Sentence Transformers},
+  doi = {10.21105/joss.08183},
+  url = {https://doi.org/10.21105/joss.08183},
+  year = {2025},
+  publisher = {The Open Journal},
+  volume = {10},
+  number = {111},
+  pages = {8183},
+  author = {Kardos, Márton and Enevoldsen, Kenneth C. and Kostkan, Jan and Kristensen-McLachlan, Ross Deans and Rocca, Roberta},
+  journal = {Journal of Open Source Software} 
+}
+```
+
 ## API Reference
 
 ::: turftopic.models.gmm.GMM

docs/KeyNMF.md

@@ -1,6 +1,6 @@
 # KeyNMF
 
-KeyNMF is a topic model that relies on contextually sensitive embeddings for keyword retrieval and term importance estimation,
+KeyNMF (Kristensen-McLachlan et al., 2024) is a topic model that relies on contextually sensitive embeddings for keyword retrieval and term importance estimation,
 while taking inspiration from classical matrix-decomposition approaches for extracting topics.
 
 <figure>
@@ -513,6 +513,44 @@ model = KeyNMF(10, encoder=encoder)
 
 Setting the default prompt to `query` is especially important, when you are precomputing embeddings, as `query` should always be your default prompt to embed documents with.
 
+## Citation
+
+Please cite Turftopic and Kristensen-McLachlan et al. (2024) when using KeyNMF:
+
+```bibtex
+@article{
+  Kardos2025,
+  title = {Turftopic: Topic Modelling with Contextual Representations from Sentence Transformers},
+  doi = {10.21105/joss.08183},
+  url = {https://doi.org/10.21105/joss.08183},
+  year = {2025},
+  publisher = {The Open Journal},
+  volume = {10},
+  number = {111},
+  pages = {8183},
+  author = {Kardos, Márton and Enevoldsen, Kenneth C. and Kostkan, Jan and Kristensen-McLachlan, Ross Deans and Rocca, Roberta},
+  journal = {Journal of Open Source Software} 
+}
+
+@inproceedings{keynmf,
+  title = "Context is Key(NMF):: Modelling Topical Information Dynamics in Chinese Diaspora Media",
+  abstract = "Does the People{\textquoteright}s Republic of China (PRC) interfere with European elections through ethnic Chinese diaspora media? This question forms the basis of an ongoing research project exploring how PRC narratives about European elections are represented in Chinese diaspora media, and thus the objectives of PRC news media manipulation. In order to study diaspora media ef{\"I}ciently and at scale, it is necessary to use techniques derived from quantitative text analysis, such as topic modelling. In this paper, we present a pipeline for studying information dynamics in Chinese media. Firstly, we present KeyNMF, a new approach to static and dynamic topic modelling using transformer-based contextual embedding models. We provide benchmark evaluations to demonstrate that our approach is competitive on a number of Chinese datasets and metrics. Secondly, we integrate KeyNMF with existing methods for describing information dynamics in complex systems. We apply this pipeline to data from five news sites, focusing on the period of time leading up to the 2024 European parliamentary elections. Our methods and results demonstrate the effectiveness of KeyNMF for studying information dynamics in Chinese media and lay groundwork for further work addressing the broader research questions.",
+  keywords = "Chinese, contextual topic models, information dynamics, keywords, novelty",
+  author = "Kristensen-McLachlan, \{Ross Deans\} and Hicke, \{Rebecca Marie Matouschek\} and M{\'a}rton Kardos and Mette Thun{\o}",
+  year = "2024",
+  month = dec,
+  language = "English",
+  volume = "3834",
+  series = "CEUR Workshop Proceedings",
+  publisher = "CEUR-WS",
+  pages = "829--847",
+  editor = "Haverals, \{Wouter \} and Koolen, \{Marijn \} and Thompson, \{Laure \}",
+  booktitle = "Proceedings of the Computational Humanities Research Conference 2024",
+  address = "Germany",
+}
+```
+
+
 ## API Reference
 
 ::: turftopic.models.keynmf.KeyNMF

docs/SensTopic.md

@@ -162,6 +162,26 @@ model.print_topics()
 | 4 | tennis, competing, federer, wimbledon, iaaf, olympic, tournament, athlete, rugby, olympics |
 | 5 | gdp, stock, economy, earnings, investments, investment, invest, exports, finance, economies |
 
+## Citation
+
+Please cite Turftopic when using the SensTopic model:
+
+```bibtex
+@article{
+  Kardos2025,
+  title = {Turftopic: Topic Modelling with Contextual Representations from Sentence Transformers},
+  doi = {10.21105/joss.08183},
+  url = {https://doi.org/10.21105/joss.08183},
+  year = {2025},
+  publisher = {The Open Journal},
+  volume = {10},
+  number = {111},
+  pages = {8183},
+  author = {Kardos, Márton and Enevoldsen, Kenneth C. and Kostkan, Jan and Kristensen-McLachlan, Ross Deans and Rocca, Roberta},
+  journal = {Journal of Open Source Software} 
+}
+```
+
 
 ## API Reference
 

docs/Topeax.md

@@ -1,6 +1,6 @@
 # Topeax
 
-Topeax is a probabilistic topic model based on the Peax clustering model, which finds topics based on peaks in point density in the embedding space. The model can recover the number of topics automatically.
+Topeax (Kardos, 2026) is a probabilistic topic model based on the Peax clustering model, which finds topics based on peaks in point density in the embedding space. The model can recover the number of topics automatically.
 
 In the following example I run a Topeax model on the BBC News corpus, and plot the steps of the algorithm to inspect how our documents have been clustered and why:
 
@@ -123,6 +123,36 @@ topeax.plot_components_datamapplot()
   <figcaption> Figure 5: Datapoints colored by mixture components on a datamapplot. </figcaption>
 </figure>
 
+## Citation
+
+Please cite Turftopic and Kardos (2026) when using the Topeax model:
+
+```bibtex
+@article{
+  Kardos2025,
+  title = {Turftopic: Topic Modelling with Contextual Representations from Sentence Transformers},
+  doi = {10.21105/joss.08183},
+  url = {https://doi.org/10.21105/joss.08183},
+  year = {2025},
+  publisher = {The Open Journal},
+  volume = {10},
+  number = {111},
+  pages = {8183},
+  author = {Kardos, Márton and Enevoldsen, Kenneth C. and Kostkan, Jan and Kristensen-McLachlan, Ross Deans and Rocca, Roberta},
+  journal = {Journal of Open Source Software} 
+}
+
+@misc{kardos2026topeaximprovedclustering,
+      title={Topeax -- An Improved Clustering Topic Model with Density Peak Detection and Lexical-Semantic Term Importance}, 
+      author={Márton Kardos},
+      year={2026},
+      eprint={2601.21465},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2601.21465}, 
+}
+```
+
 ## API Reference
 
 ::: turftopic.models.topeax.Topeax

docs/clustering.md

@@ -403,7 +403,50 @@ _See Figure 1_
 !!! info
     If you are not running Turftopic from a Jupyter notebook, make sure to call `fig.show()`. This will open up a new browser tab with the interactive figure.
 
+## Citation
+
+Please cite Turftopic when using clustering models:
+
+```bibtex
+
+@article{
+  Kardos2025,
+  title = {Turftopic: Topic Modelling with Contextual Representations from Sentence Transformers},
+  doi = {10.21105/joss.08183},
+  url = {https://doi.org/10.21105/joss.08183},
+  year = {2025},
+  publisher = {The Open Journal},
+  volume = {10},
+  number = {111},
+  pages = {8183},
+  author = {Kardos, Márton and Enevoldsen, Kenneth C. and Kostkan, Jan and Kristensen-McLachlan, Ross Deans and Rocca, Roberta},
+  journal = {Journal of Open Source Software} 
+}
+```
 
+In addition, cite Grootendorst (2022) or Angelov (2020) when using BERTopic or Top2Vec respectively:
+
+```bibtex
+@misc{grootendorst2022bertopicneuraltopicmodeling,
+      title={BERTopic: Neural topic modeling with a class-based TF-IDF procedure}, 
+      author={Maarten Grootendorst},
+      year={2022},
+      eprint={2203.05794},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2203.05794}, 
+}
+
+@misc{angelov2020top2vecdistributedrepresentationstopics,
+      title={Top2Vec: Distributed Representations of Topics}, 
+      author={Dimo Angelov},
+      year={2020},
+      eprint={2008.09470},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2008.09470}, 
+}
+```
 
 ## API Reference
 

docs/ctm.md

@@ -1,6 +1,6 @@
 # Variational Autoencoding Topic Models
 
-Topic models based on Variational Autoencoding are generative models based on ProdLDA (citation) enhanced with contextual representations.
+Topic models based on Variational Autoencoding are generative models based on ProdLDA (Bianchi et al, 2021) enhanced with contextual representations.
 
 <figure>
   <img src="../images/CTM_plate.png" width="60%" style="margin-left: auto;margin-right: auto;">
@@ -56,6 +56,46 @@ This has a number of implications, most notably:
 
 Turftopic, similarly to Clustering models might not contain some model specific utilites, that CTM boasts.
 
+## Citation
+
+Please cite Turftopic and Bianchi et al. (2021) when using Autoencoding models in Turftopic
+```bibtex
+
+@article{
+  Kardos2025,
+  title = {Turftopic: Topic Modelling with Contextual Representations from Sentence Transformers},
+  doi = {10.21105/joss.08183},
+  url = {https://doi.org/10.21105/joss.08183},
+  year = {2025},
+  publisher = {The Open Journal},
+  volume = {10},
+  number = {111},
+  pages = {8183},
+  author = {Kardos, Márton and Enevoldsen, Kenneth C. and Kostkan, Jan and Kristensen-McLachlan, Ross Deans and Rocca, Roberta},
+  journal = {Journal of Open Source Software} 
+}
+
+@inproceedings{bianchi-etal-2021-pre,
+    title = "Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence",
+    author = "Bianchi, Federico  and
+      Terragni, Silvia  and
+      Hovy, Dirk",
+    editor = "Zong, Chengqing  and
+      Xia, Fei  and
+      Li, Wenjie  and
+      Navigli, Roberto",
+    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.acl-short.96/",
+    doi = "10.18653/v1/2021.acl-short.96",
+    pages = "759--766",
+    abstract = "Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data. However, the resulting word groups are often not coherent, making them harder to interpret. Recently, neural topic models have shown improvements in overall coherence. Concurrently, contextual embeddings have advanced the state of the art of neural models in general. In this paper, we combine contextualized representations with neural topic models. We find that our approach produces more meaningful and coherent topics than traditional bag-of-words topic models and recent neural models. Our results indicate that future improvements in language models will translate into better topic models."
+}
+```
+
 ## API Reference
 
 ::: turftopic.models.ctm.AutoEncodingTopicModel

docs/cvp.md

@@ -71,6 +71,39 @@ print(concept_df)
 1  0.269454  0.009495
 ```
 
+## Citation
+
+Please cite Lyngbæk et al. (2025) and Turftopic when using Concept Vector Projection in publications:
+
+```bibtex
+@article{
+  Kardos2025,
+  title = {Turftopic: Topic Modelling with Contextual Representations from Sentence Transformers},
+  doi = {10.21105/joss.08183},
+  url = {https://doi.org/10.21105/joss.08183},
+  year = {2025},
+  publisher = {The Open Journal},
+  volume = {10},
+  number = {111},
+  pages = {8183},
+  author = {Kardos, Márton and Enevoldsen, Kenneth C. and Kostkan, Jan and Kristensen-McLachlan, Ross Deans and Rocca, Roberta},
+  journal = {Journal of Open Source Software} 
+}
+
+@incollection{Lyngbaek2025,
+  title = {Continuous Sentiment Scores for Literary and Multilingual
+Contexts},
+  author = {Laurits Lyngbaek and Pascale Feldkamp and Yuri Bizzoni and Kristoffer L. Nielbo and Kenneth Enevoldsen},
+  year = {2025},
+  booktitle = {Computational Humanities Research 2025},
+  publisher = {Anthology of Computers and the Humanities},
+  pages = {480--497},
+  editor = {Taylor Arnold and Margherita Fantoli and Ruben Ros},
+  doi = {10.63744/nVu1Zq5gRkuD}
+}
+```
+
+
 ## API Reference
 
 

docs/s3.md

@@ -202,6 +202,49 @@ fig.show()
   <figcaption> Image Compass of IKEA furnitures along two semantic axes </figcaption>
 </figure>
 
+## Citation
+
+Please cite Turftopic and Kardos et al. (2025) when using $S^3$ in Turftopic:
+```bibtex
+
+@article{
+  Kardos2025,
+  title = {Turftopic: Topic Modelling with Contextual Representations from Sentence Transformers},
+  doi = {10.21105/joss.08183},
+  url = {https://doi.org/10.21105/joss.08183},
+  year = {2025},
+  publisher = {The Open Journal},
+  volume = {10},
+  number = {111},
+  pages = {8183},
+  author = {Kardos, Márton and Enevoldsen, Kenneth C. and Kostkan, Jan and Kristensen-McLachlan, Ross Deans and Rocca, Roberta},
+  journal = {Journal of Open Source Software} 
+}
+
+@inproceedings{kardos-etal-2025-s3,
+    title = "$S^3$ - Semantic Signal Separation",
+    author = "Kardos, M{\'a}rton  and
+      Kostkan, Jan  and
+      Enevoldsen, Kenneth  and
+      Vermillet, Arnault-Quentin  and
+      Nielbo, Kristoffer  and
+      Rocca, Roberta",
+    editor = "Che, Wanxiang  and
+      Nabende, Joyce  and
+      Shutova, Ekaterina  and
+      Pilehvar, Mohammad Taher",
+    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2025",
+    address = "Vienna, Austria",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.acl-long.32/",
+    doi = "10.18653/v1/2025.acl-long.32",
+    pages = "633--666",
+    ISBN = "979-8-89176-251-0",
+    abstract = "Topic models are useful tools for discovering latent semantic structures in large textual corpora. Recent efforts have been oriented at incorporating contextual representations in topic modeling and have been shown to outperform classical topic models. These approaches are typically slow, volatile, and require heavy preprocessing for optimal results. We present Semantic Signal Separation ($S^3$), a theory-driven topic modeling approach in neural embedding spaces. $S^3$ conceptualizes topics as independent axes of semantic space and uncovers these by decomposing contextualized document embeddings using Independent Component Analysis. Our approach provides diverse and highly coherent topics, requires no preprocessing, and is demonstrated to be the fastest contextual topic model, being, on average, 4.5x faster than the runner-up BERTopic. We offer an implementation of $S^3$, and all contextual baselines, in the Turftopic Python package."
+}
+```
 
 ## API Reference