Update README.md

README.md

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 🤖 **Model description**
 
-This model was trained on ~25k heterogeneous manually annotated sentences (📚 Stab et al. 2018) of controversial topics to classify text into one of two labels: 🏷 **NON-ARGUMENT** (0) and **ARGUMENT** (1).
+This model was trained on ~25k heterogeneous manually annotated sentences (📚 [Stab et al. 2018](https://www.aclweb.org/anthology/D18-1402/)) of controversial topics to classify text into one of two labels: 🏷 **NON-ARGUMENT** (0) and **ARGUMENT** (1).
 
 🗃 **Dataset**
 
-The dataset (📚 Stab et al. 2018) consists of **ARGUMENTS** (\~11k) that either support or oppose a topic if it includes a relevant reason for supporting or opposing the topic, or as a **NON-ARGUMENT** (\~14k) if it does not include reasons. The authors focus on controversial topics, i.e., topics that include an obvious polarity to the possible outcomes and compile a final set of eight controversial topics: _abortion, school uniforms, death penalty, marijuana legalization, nuclear energy, cloning, gun control, and minimum wage_.
+The dataset (📚 Stab et al. 2018) consists of **ARGUMENTS** (\\~11k) that either support or oppose a topic if it includes a relevant reason for supporting or opposing the topic, or as a **NON-ARGUMENT** (\\~14k) if it does not include reasons. The authors focus on controversial topics, i.e., topics that include an obvious polarity to the possible outcomes and compile a final set of eight controversial topics: _abortion, school uniforms, death penalty, marijuana legalization, nuclear energy, cloning, gun control, and minimum wage_.
 
 | TOPIC | ARGUMENT | NON-ARGUMENT |
 |----|----|----|
@@ -58,6 +58,4 @@ Showing the **confusion matrix** using the 20% of the sentences as an evaluation
 
 The model can be a starting point to dive into the exciting area of argument mining. But be aware. An argument is a complex structure, topic-dependent, and often differs between different text types. Therefore, the model may perform less well on different topics and text types, which are not included in the training set.
 
-Enjoy and stay tuned! 🚀
-
-📚Stab et al. (2018): Cross-topic Argument Mining from Heterogeneous Sources. [LINK](https://www.aclweb.org/anthology/D18-1402/).
+Enjoy and stay tuned! 🚀