Ive been able to use a set of regex rules that feed a scoring system to profile Pubmed abstracts. For example, any instance of 'increased risk', 'increased association', etc., adds to an 'association' counter. Similarly, 'reduced risk' subtracts from the same counter.
I can also identify and extract specifics such as statistical data measures (eg p-values), gene names, sample population/patient characteristics, etc.
Of course this approach is not valid for complex grammar but it lends itself to the predictable and formulaic format that abstracts are written in, and in particular to the discipline that I am interested in, ie molecular biology.
I was surprised at how useful such a simple methodology can be for profiling an abstract.
The key is in developing the regex rules. For my application, ie extracting data from Pubmed abstracts, I was essentially modelling my internal process of what I look for when scanning abstracts to determine relevance.
Over time I found I was adding new phrases to the regex 'library' to capture instances where the rule system encountered text it did not profile correctly.