This article describes two types of summarization approaches integrated in a flexible architecture for multitask summarization. The first type is based on the use of lexical features, while the second one is grounded on syntactic and semantic information. All the approaches have been evaluated in experiments where, given a set of documents, they are expected to produce summaries answering a user need (expressed by a query) in a reduced set of relevant textual fragments. Their performance is analyzed in two different tasks: written news and scientific oral presentations.