4. The method of claim 1 , wherein each of the training segments of the training set is assigned an assumed salience score based on the user interaction signals corresponding to the training segment.
5. The method of claim 4 , wherein, for each of the training segments, the machine-learned model is trained by generating a function that predicts the assumed salience score for the training segment using the audio-visual content features of the training segment as inputs to the function.