Update README.md

README.md

@@ -17,33 +17,10 @@ source_datasets:
 - original
 task_categories:
 - text-classification
-task_ids:
-- multi-label-classification
 ---
 ## Data Description
-Long-COVID related articles have been manually collected by information specialists. As a certain amount of data is needed
-to train a deep learning-based model, data from two different
-sources have been merged in case of positive examples. To get
-further negative examples, we used a third resource.The first subset was provided by information specialists
-from the Robert Koch Institute and has been collected in the
-following way:
-Katharina
-The second subset is retrieved from the ”Long covid research
-library” released by Pandemic-Aid Networks, who collect
-”important papers that have been published on Long Covid” [2].
-We retrieved 195 articles on January 4th, 2022. As these are all
-positive examples, we needed further negative examples to have
-a balanced training data set. Therefore, we used the database
-LitCovid [5, 6] and filtered for non-long-COVID articles (query:
-NOT e condition:LongCovid) and retrieved further 62 articles.
-As we wanted to train a model on manually curated data rather
-than using semi-automatically classified data - as implemented
-in LitCovid - we did not include further documents from there.
-Preliminary experiments revealed that using more documents
-from LitCovid, also for both classes, lowers the performance,
-when evaluated on the manually curated data sets. The data
-sets have been merged, shuffled randomly and split into training,
-development and test sets.
+Long-COVID related articles have been manually collected by information specialists.  
+Further information and citation coming soon.
 
 ## Size 
 ||Training|Development|Test|Total|