Complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topicoriented, informative multi-document summarization. In this paper, we have experimented with one empirical and two unsupervised statistical machine learning techniques: kmeans and Expectation Maximization (EM), for computing relative importance of the sentences. However, the performance of these approaches depends entirely on the feature set used and the weighting of these features. We extracted different kinds of features (i.e. lexical, lexical semantic, cosine similarity, basic element, tree kernel based syntactic and shallow-semantic) for each of the document sentences in order to measure its importance and relevancy to the user query. We used a local search technique to learn the weights of the features. For all our methods of generating summaries, we have shown the effects of syntactic and shallow-semantic features over the bag of words (BOW) features.