A few ideas to parse "events" from a text document
These documents can be very unstructured and machine learning rather takes perfectly structured data like a single clean table of numbers. The real hard part, how to translate all the interesting information (font size, bold, position, order, ...) into a clean table, isn't solved by machine learning. You see it's hard to create a sensible, purely numeric table from a document.

I think your best bet is to use a natural language processing toolkit and do named entity recognition. See http://www.nltk.org/book/ch07.html "5 Named entity recognition". Or see the recommendation here: https://stackoverflow.com/questions/11333903/nltk-named-entity-recognition-with-custom-data

With this auxiliary information, I'd rather spend time handcoding rules, which work on most documents. You can create a nested data structure and try to match patterns like "headline, enumitem1, enumitem2, ..."

The rules would throw errors on documents where they are unsure. For these documents you will have to manually check what's going on. But this way you don't lose information.

I'm no expert on the very advanced methods, but in theory, with a lot of labeled data, a very clever encoding schema and a fair bit of luck an approach like RNN might be successful. But even if all the mentioned requirements hold, the training of those RNNs require a lot of experience that is hard to learn from references.