  Complex questions that require inferencing and synthesizing information from
multiple documents can be seen as a kind of topic-oriented, informative
multi-document summarization where the goal is to produce a single text as a
compressed version of a set of documents with a minimum loss of relevant
information. In this paper, we experiment with one empirical method and two
unsupervised statistical machine learning techniques: K-means and Expectation
Maximization (EM), for computing relative importance of the sentences. We
compare the results of these approaches. Our experiments show that the
empirical approach outperforms the other two techniques and EM performs better
than K-means. However, the performance of these approaches depends entirely on
the feature set used and the weighting of these features. In order to measure
the importance and relevance to the user query we extract 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. We use a local search technique to learn the weights of the
features. To the best of our knowledge, no study has used tree kernel functions
to encode syntactic/semantic information for more complex tasks such as
computing the relatedness between the query sentences and the document
sentences in order to generate query-focused summaries (or answers to complex
questions). For each of our methods of generating summaries (i.e. empirical,
K-means and EM) we show the effects of syntactic and shallow-semantic features
over the bag-of-words (BOW) features.
