add citation info in readme

README.md

@@ -13,4 +13,24 @@ This dataset can be used as a benchmark for clustering word embeddings for <b>Ge
 
 The datasets contains news article titles and is based on the dataset of the [One Million Posts Corpus](https://ofai.github.io/million-post-corpus/) and [10kGNAD](https://github.com/tblock/10kGNAD). It contains 10'275 unique samples, 10 splits with 1'436 to 9'962 samples and 9 unique classes. Splits are built similarly to MTEB's [TwentyNewsgroupsClustering](https://huggingface.co/datasets/mteb/twentynewsgroups-clustering).
 
-Have a look at German Text Embedding Clustering Benchmark ([Github](https://github.com/ClimSocAna/tecb-de), [Paper](https://arxiv.org/abs/2401.02709)) for more infos, datasets and evaluation results.
+Have a look at German Text Embedding Clustering Benchmark ([Github](https://github.com/ClimSocAna/tecb-de), [Paper](https://arxiv.org/abs/2401.02709)) for more infos, datasets and evaluation results.
+
+```
+@inproceedings{wehrli-etal-2023-german,
+    title = "{G}erman Text Embedding Clustering Benchmark",
+    author = "Wehrli, Silvan  and
+      Arnrich, Bert  and
+      Irrgang, Christopher",
+    editor = "Georges, Munir  and
+      Herygers, Aaricia  and
+      Friedrich, Annemarie  and
+      Roth, Benjamin",
+    booktitle = "Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)",
+    month = sep,
+    year = "2023",
+    address = "Ingolstadt, Germany",
+    publisher = "Association for Computational Lingustics",
+    url = "https://aclanthology.org/2023.konvens-main.20",
+    pages = "187--201",
+}
+```