update readme

README.md

@@ -16,3 +16,30 @@ datasets: "fhamborg/newsmtsc"
 
 ---
 
+# NewsSentiment: easy-to-use, high-quality target-dependent sentiment classification for news articles
+
+This model is also available as an easy-to-use PyPI package named [`NewsSentiment`](https://pypi.org/project/NewsSentiment/) and 
+in its original GitHub repository named [`NewsMTSC`](https://github.com/fhamborg/NewsMTSC), where you will find the dataset the model was trained on, other models for sentiment classification, and a training and testing framework. More information on the model and the dataset (consisting of more than 10k sentences sampled from news articles, each 
+labeled and agreed upon by at least 5 annotators) can be found in our [EACL paper](https://aclanthology.org/2021.eacl-main.142.pdf). The
+dataset, the model, and its source code can be viewed in our [GitHub repository](https://github.com/fhamborg/NewsMTSC).
+
+The model is the currently [best performing](https://aclanthology.org/2021.eacl-main.142.pdf) 
+targeted sentiment classifier for news articles. In contrast to regular sentiment
+classification, targeted sentiment classification allows you to provide a target in a sentence. 
+Only for this target, the sentiment is then predicted. This is more reliable in many
+cases, as demonstrated by the following simplistic example: "I like Bert, but I hate Robert."
+
+
+# How to cite
+If you use the dataset or model, please cite our [paper](https://www.aclweb.org/anthology/2021.eacl-main.142/) ([PDF](https://www.aclweb.org/anthology/2021.eacl-main.142.pdf)):
+
+```
+@InProceedings{Hamborg2021b,
+  author    = {Hamborg, Felix and Donnay, Karsten},
+  title     = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles},
+  booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)},
+  year      = {2021},
+  month     = {Apr.},
+  location  = {Virtual Event},
+}
+```