Can you articulate why this is different to other text summarising solutions?
It may be that yours is easier to integrate than using AWS APIs[1], or performs better than what's available on say, npm[2]. It may be that your algorithm is designed specifically for news articles.
If you can articulate where this fits into the market of other solutions - that will help inform how best to utilize it.
I’m not sure how relevant it is. Dropbox entered into a market with around 10 competitors, and trounced them all. In general it’s a mistake to worry about the competition.
Everyone else is focusing on news articles as well, so that's not a problem; however without comparison to other approaches it's impossible to tell if the solution is 'powerful' or even any good at all.
This here - http://nlpprogress.com/english/summarization.html - would be an overview of what algorithms are currently considered 'good' and what results they achieve on some datasets commonly used for evaluation and comparison, it would be interesting to run your solution on that data and see what you get.
It may be that yours is easier to integrate than using AWS APIs[1], or performs better than what's available on say, npm[2]. It may be that your algorithm is designed specifically for news articles.
If you can articulate where this fits into the market of other solutions - that will help inform how best to utilize it.
[1] https://aws.amazon.com/blogs/machine-learning/part-1-set-up-...
[2] https://www.npmjs.com/package/text-summary