Question: We have ~500 biomedical documents each of some 1-2 MB. We want to use a non query-based method to rank the documents in order of their unique content score. I'm calling it "unique content" because our researchers want to know from which document to start reading. All the documents are of the same topic, in the biomedical world we know that there is always a lot of content overlap. So all we want to do is to arrange the documents in the order of their unique content.

Most Information Retrieval literature suggest query-based ranking which does not fit our need.

Answer: 
You could use Topic Modeling as described in this paper:
<http://faculty.chicagobooth.edu/workshops/orgs-markets/pdf/KaplanSwordWin2014.pdf>

They performed Topic Modeling on abstracts of patents (limited to 150 words). They identified papers as "novel" if they were the first to introduce a topic, and measured degree of novelty by how many papers in the following year used the same topic. (Read the paper for details).

I suggest that you follow their lead and only process paper abstracts. Processing the body of each paper might reveal some novelty that the abstract does not, but you also run the risk of having much more noise in your topic model (i.e. extraneous topics, extraneous words).

While you say that all 500 papers are on the same "topic", it's probably safer to say that they are all on the same "theme" or in the same "sub-category" of Bio-medicine. Topic modeling permits decomposition of the "theme" into "topics".

The good news is that there are plenty of good packages/libraries for Topic Modeling. You still have to do preprocessing, but you don't have to code the algorithms yourself. See this page for many resources:
<http://www.cs.princeton.edu/~blei/topicmodeling.html>
Here's a simple initial approach to try:

1. Calculate the [TF-IDF](http://en.wikipedia.org/wiki/Tf%E2%80%93idf) score of each word in each document.
2. Sort the documents by the average TF-IDF score of their words.
3. The higher the average TF-IDF score, the more unique a document is with respect to the rest of the collection.

You might also try a clustering-based approach where you look for outliers, or perhaps something with the [Jaccard index](http://en.wikipedia.org/wiki/Jaccard_index) using a bag-of-words model.