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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.

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If you have data with a good score system, I would start with something simple, because using a neural network like Bert might be complex to set up.

Something simple is to take the scores and build a phrase with meaning, for instance: "solar panel" + "rooftop" + "environment-friendly" = "Rooftop solar panel, with a low environmental impact (less than 8g of carbon/year)".

You can achieve this using if/then rules and some basic equations if there is numerical values. For example, 0.2 for the environmental impact would be something like (1-0.2)*10 = 8g.

Then you can improve results with a neural network like Bert, but you would need enough data to train it, using different inputs ("0.2,0.6,0.1") and their associated outputs (-> "Rooftop solar panel, with a low environmental impact (less than 8g of carbon/year)") and this train data should be representative enough of most common use cases.

See: https://chriskhanhtran.github.io/posts/extractive-summarization-with-bert/

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    $\begingroup$ Thanks for your suggestions, @Nicolas. It looks as though there already exists a transformer library (gagan3012/keytotext) that achieves more or less what I want it to; that is, abstractive text summarisation from keywords alone. However I think it would achieve much higher fidelity if I were to use an approach like yours first before calling the keytotext function. I imagine a multi-layered approach would offer the best possible outcome. $\endgroup$
    – Jeff
    Oct 25, 2022 at 10:51
  • $\begingroup$ Good to know. Thanks @Jeff $\endgroup$ Oct 25, 2022 at 12:56
  • $\begingroup$ If the answer is correct, could you validate it @Jeff ? $\endgroup$ Oct 29, 2022 at 14:47

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