Suppose I have a list of weighted keywords/phrases, such as "solar panel", "rooftop", etc. The weights are in [0,1] with higher weights indicating a stronger preference for specific keywords, so "solar panel" may have a weighting of 0.3 and "rooftop" may have a weighting of 0.2, for example. The sum of keyword weights is 1.
For each keyword/phrase, I additionally have a number of contextual sentences which are also weighted and carry a positive, negative, or neutral sentiment/connotation. For example, one contextual sentence related to the "solar panel" phrase might be "good for the environment" which is labelled with a positive sentiment and carries a weight of 0.2. The sum of weights for each keyword's contextual sentences is 1, so the sum of weights for all contextual sentences across all keywords is N, where N is the number of individual keywords.
Finally, I also have weighted linkages in [0,1] between keywords/phrases which, again, sum to 1. For example, the directed linkage from "solar panel" to "rooftop" may have a weight of 0.2 while the directed linkage from "rooftop" to "solar panel" may have a weight of 0.4.
I would like to use these weighted keywords, phrases, contextual sentiment-labelled sentences and linkages to create a summary in natural language. I realise that I'm working in reverse from the typical text summarisation objective, but I believe that the richness of my data should make the task a little easier.
How should I approach it? Should I first use a model to summarise the text contained within each of the contextual sentences before attempting to extract more basic keywords that can be used to generate summary text? How should I process the data? Is it worth pursuing a two-step approach, where a basic model summarises the keywords and contextual sentences in basic language before a secondary model transforms it to richer, more natural language?
I would be very grateful for any guidance or recommendations.
Edit: I'm very new to NLP, so I apologise for my lack of terminology and mathematical formalism.