We present a collection of algorithms to filter a stream of documents in such a way that the filtered documents will cover as well as possible the interest of a person, keeping in mind that, at any given time, the offered documents should not only be relevant, but should also be diversified, in the sense not only of avoiding nearly identical documents, but also of covering as well as possible all the interests of the person. We use a modification of the WEBSOM algorithm, with limited architectural adaptation, to create a user model (which we call the "user context" or simply the "context") based on a network of units laid out in the word space and trained using a collection of documents representative of the context. We introduce the concepts of novelty and coverage. Novelty is related to, but not identical to, the homonymous information retrieval concept: a document is novel it it belongs to a semantic area of interest to a person for which no documents have been seen in the recent past. A group of documents has coverage to the extent to which it is a good representation of all the interests of a person. In order to increase coverage, we introduce an "interest" (or "urgency") factor for each unit of the user model, modulated by the scores of the incoming documents: the interest of a unit is decreased drastically when a document arrives that belongs to its semantic area and slowly recovers its initial value if no documents from that semantic area are displayed. Our tests show that these algorithms can effectively increase the coverage of the documents that are shown to the user without overly affecting precision.