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Complex questions that require inferencing and synthesizing information frommultiple documents can be seen as a kind of topic-oriented, informativemulti-document summarization where the goal is to produce a single text as acompressed version of a set of documents with a minimum loss of relevantinformation. In this paper, we experiment with one empirical method and twounsupervised statistical machine learning techniques: K-means and ExpectationMaximization (EM), for computing relative importance of the sentences. Wecompare the results of these approaches. Our experiments show that theempirical approach outperforms the other two techniques and EM performs betterthan K-means. However, the performance of these approaches depends entirely onthe feature set used and the weighting of these features. In order to measurethe importance and relevance to the user query we extract different kinds offeatures (i.e. lexical, lexical semantic, cosine similarity, basic element,tree kernel based syntactic and shallow-semantic) for each of the documentsentences. We use a local search technique to learn the weights of thefeatures. To the best of our knowledge, no study has used tree kernel functionsto encode syntactic/semantic information for more complex tasks such ascomputing the relatedness between the query sentences and the documentsentences in order to generate query-focused summaries (or answers to complexquestions). For each of our methods of generating summaries (i.e. empirical,K-means and EM) we show the effects of syntactic and shallow-semantic featuresover the bag-of-words (BOW) features.

Complex Question Answering: Unsupervised Learning Approaches and Experiments