1. Split your text into sentences
2. Remove stopwords (keeping a copy of the original sentence)
3. For the remaining words, calculate the synthetic TF-IDF score of all the words in the sentence (tf-idf each word then sum them).
4. Keep the n-highest scoring sentences (in the order they appear), or all the sentences with synthetic TF-IDF scores above some threshold.
There's your summary.
BTW, I love the highlight-in-the-original-text option.
> How did it work? Any interesting tweaks you can share?
The analysis code (https://github.com/peterldowns/bookshrink/blob/master/analys...) is very short and the comments include my thoughts on certain tweaks / approaches. Some quirks of the implementation: uses regex for sentence splitting (really!), doesn't perform any stemming, and weights proper nouns heavier than other words.
> BTW, I love the highlight-in-the-original-text option.
There's a bit of art to it for sure that can improve the results. You may also have to do some pronoun substitution in the summarized sentences (and then decide to do that before or after calculating the synthetic score) so they make more sense.
I find it's hard to tell how well all this will work until you just do it. Not every kind of text works equally well and the only proof that it's really working is "does the summary make sense or not?"
It's also possible that for long structured documents like laws or contracts, that you don't want to summarize the whole thing, but treat major sections like different documents and do intra-document summarization to maintain understandability.
Here's one that does something kinda like what I was writing about above.
note I think the correct measure is not TF-IDF, but TF-ISF (Inverse sentence frequency) for single document summarization, but I might be wrong.
related is the idea of "stemming" which uses an algorithm to try to reduce inflection, to find a common form of a word that various versions come from. Porters algorithm is a well known stemming algorithm. However, sometimes you end up with weird "non-inflected" tokens at the end. (e.g. 'enhancement' might become 'enhanc')
However, lemmatization is considered "better" in that it uses a dictionary of inflected forms that map back to the non-inflected form. So in theory, if the dictionary is comprehensive, you can properly replace inflected forms with their correct non-inflected forms. (e.g. 'enhancement' -> 'enhance')
If your dictionary isn't comprehensive and comes across a token it doesn't recognize, you can try falling back onto a stemming algorithm.
http://www.cs.ubc.ca/labs/imager/tr/2014/Overview/
It includes a number of the data mining techniques others have mentioned and implements some nice design guidelines from visual analytics.
I'm working on a project to make (EU-) law more accessible. So if anybody here knows good methods to visualise/summarise long legal texts (30-300 pages) you could do something for humanity by posting a reply.
(Word clouds just don't cut it in these cases.)