There is no brute force strategy that is going to help you solve this problem. The reason for this is that there are a very large number of ways to express the same idea in language and whatever solution you're using has to take that into account. The most common "solutions" out there tend to restrict their summarization to a specific domain, which helps because you can then look for things like keywords (your 'trigger words') to help you.

If your specific case involves connecting declarations to cities, you could create a rule like this:

while sentence contains CITY + VERB # Assuming subject + verb + object
+ (SVO) word order
    define TOPIC as CITY
until sentence contains ANOTHER_CITY + VERB
[download]

But unfortunately, that's about as far as you get using "simple" methods. The problem here is that English has other ways of defining topics than just having the topic be the syntactic subject argument of the verb. So you have to have something which more or less "understands" your target language before you can summarize. There can really be no randomness about it. There is a project, however, that uses a statistical NLP method called "clustering" to get decent results in a general topic domain.

Have a look at the source of the MEAD summarizer to see how a statistical summarizer works (It's written in Perl). You can also see what a such a summarization engine produces at News in Essence, a news-domain summarizer. My personal preference would be a knowledge-based approach using a chunker (or "shallow parser") to get at the semantically relevant bits of the text.