I would like to create a topic distribution for a document.
The current model I am trying to implement is: for each sentence in the document, I am getting a topic assignment with a score, e.g. "1st sentence is about Microsoft with a relevance score of 0.4". I repeat this for each sentence, and at the end I have relevance scores with the topics like the following:
1st sentence: microsoft, score 0.4
2nd sentence: apple, score: 0.1
3rd sentence: android, score: 0.5
Now, I would like to convert these scores into 1 big probability distribution that will represent the whole document. Is there a known technique to do this? If so, what is the best way to do this?
Note: I know this is a very naive topic modelling, but I am currently interested in combining the scores into prob. distributions.