I am writing a tool that will give users the ability to summarize text content on a webpage, by highlighting the text that they wish to get summarized.

So far, I've received results that I can work with in order to further optimize the algorithm, but only when applied to single paragraphs. When multiple paragraphs are selected, the summary isn't very successful in that it usually focuses on a certain major part of the selected text input, ignoring other parts that should also be mentioned in the summary. This is because the entire algorithm functions around determining the most important sentence of a paragraph (or in this case, multiple paragraphs combined in one big "paragraph"), and then determining which other sentences are related/of importance to this "core sentence". The algorithm uses extractive automatic summarization ([explanation on WikiPedia](https://en.wikipedia.org/wiki/Automatic_summarization#Extraction-based_summarization)).

As a single paragraph usually handles one major subject, this algorithm works alright in such cases but when a big text with multiple paragraphs (and thus usually multiple major subjects), important parts are missing from the resulting summary.

My question is, how can I achieve succinct and qualitative summary results when handling a text with multiple paragraphs when using my "core sentence" approach without resorting to an algorithm such as `TextRank` (which works good but delivers summaries that are too long, often containing 60-70% of the original text) - or should I force my users to summarize paragraph per paragraph (as I haven't found a trustworthy way yet to correctly determine paragraphs in a text when there are no `\n\n` characters or `<p>`-tags between the paragraphs to split the input on).