Do you know good papers about strategies of how to select keywords effectivly beyond the scope of stopword lists and stemming?
Using term frequencies of the document is not really possible since lucene is not providing access to a document vector, isn't it?
Lucene does let you access the document frequency of terms, with IndexReader.docFreq(). Term frequencies can be computed by re-tokenizing the text, which, for a single document, is usually fast enough. But looking up the docFreq() of every term in the document is probably too slow.
You can use some heuristics to prune the set of terms, to avoid calling docFreq() too much, or at all. Since you're trying to maximize a tf*idf score, you're probably most interested in terms with a high tf. Choosing a tf threshold even as low as two or three will radically reduce the number of terms under consideration. Another heuristic is that terms with a high idf (i.e., a low df) tend to be longer. So you could threshold the terms by the number of characters, not selecting anything less than, e.g., six or seven characters. With these sorts of heuristics you can usually find small set of, e.g., ten or fewer terms that do a pretty good job of characterizing a document.
It all depends on what you're trying to do. If you're trying to eek out that last percent of precision and recall regardless of computational difficulty so that you can win a TREC competition, then the techniques I mention above are useless. But if you're trying to provide a "more like this" button on a search results page that does a decent job and has good performance, such techniques might be useful.
An efficient, effective "more-like-this" query generator would be a great contribution, if anyone's interested. I'd imagine that it would take a Reader or a String (the document's text), an Analyzer, and return a set of representative terms using heuristics like those above. The frequency and length thresholds could be parameters, etc.
Doug
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