added paper reference in intro text

src/about.py

@@ -49,9 +49,8 @@ LOGO = """<img src="file/assets/image.png" alt="Clinical X HF" width="500" heigh
 INTRODUCTION_TEXT = """
 The main goal of the Named Clinical Entity Recognition Leaderboard is to evaluate and benchmark the performance of various language models in accurately identifying and classifying named clinical entities across diverse medical domains. This task is crucial for advancing natural language processing (NLP) applications in healthcare, as accurate entity recognition is foundational for tasks such as information extraction, clinical decision support, and automated documentation.  
 
-The datasets used for this evaluation encompass a wide range of medical entities, including diseases, symptoms, medications, procedures and anatomical terms. These datasets are sourced from openly available clinical data (including annotations) to ensure comprehensive coverage and reflect the complexity of real-world medical language. More details about the datasets included can be found below ("About" section).  
-
-The evaluation metrics used in this leaderboard focus primarily on the F1-score, a widely recognized measure of a model's accuracy. The different modes of evaluation are also described below.  
+The datasets used for this evaluation encompass a wide range of medical entities, including diseases, symptoms, medications, procedures and anatomical terms. These datasets are sourced from openly available clinical data (including annotations) to ensure comprehensive coverage and reflect the complexity of real-world medical language. The evaluation metrics used in this leaderboard focus primarily on the F1-score, a widely recognized measure of a model's accuracy. 
+More details about the datasets and metrics can be found below in the 'About' section and the [NCER paper](https://arxiv.org/abs/2410.05046).  
 
 Disclaimer: It is important to note that the purpose of this evaluation is purely academic and exploratory. The models assessed here have not been approved for clinical use, and their results should not be interpreted as clinically validated. The leaderboard serves as a platform for researchers to compare models, understand their strengths and limitations, and drive further advancements in the field of clinical NLP.
 """