Update README.md

README.md

@@ -25,19 +25,33 @@ It achieves the following results on the evaluation set:
 - Rougelsum: 0.3525
 - Gen Len: 55.882
 
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+### Project Purpose
+
+Our goal is to deliver an effective summarization solution aimed at making doctor discharge notes more structured and comprehensive.
+A physician's job goes far beyond saving lives, doctors are also responsible for providing a comforting environment for their patients.
+With that in mind, while accommodating in a high-stress environment it is difficult to follow a structure and formulate notes with universal interpretability in mind.
+This leads to long and convoluted discharge documentation that becomes very tedious to leverage and understand. Our model is a product that will alleviate 
+a significant amount of discomfort when creating and utilizing physician notes, which ultimately will lead to more fluid workflows and increased convenience for healthcare providers.
+
+### Intended Use
+#### Model
+We leveraged Google's Pegasus abstractive text summarization to generate summaries of the discharged transcriptions included in the MTSamples dataset. 
+This was later utilized to prompt the Transformer's Masked Language Modeling(MLM) functionality to train the model to generate meaningful text with better structure and organization than the original.
+Additionally, Data Engineers that work with patient electronic records consistently spend an excessive amount of time parsing through the unstructured discharge notes format to accomplish their tasks.
+The solution will be instrumental for agents who are not directly facing patients but hold back-end roles that are also of immense importance.
+
+Data Engineer?
+#### Use Cases
+This model allows for the efficient summarization of complexly documented doctor notes. It provides instant access to insight with proper semantic cues in place.
+
+##### Limitations & Future Aspirations
+With an increased amount of data, more deliberate results might be achieved through more training. Also, further improvements on the model's summarization capabilities have been considered.
+One of which is implementing summarization based on clustered titles within the discharge notes. The feature would allow for easier traversal through partitioned summarization and result in better structure.
+
+##### Training and evaluation data
+The generated summaries were assigned to the original transcription and after splitting the data into the train and test sets, the table was converted into a json file.
+The structure allowed us to effectively train the model on the premise of transcription to summarization prompts. After all the metrics were evaluated, a number of medical transcriptions were generated through
+generative transformers to summarize and upon testing the model performed well.  
 
 ### Training hyperparameters