  Manifold ranking has been successfully applied in query-oriented
multi-document summarization. It not only makes use of the relationships among
the sentences, but also the relationships between the given query and the
sentences. However, the information of original query is often insufficient. So
we present a query expansion method, which is combined in the manifold ranking
to resolve this problem. Our method not only utilizes the information of the
query term itself and the knowledge base WordNet to expand it by synonyms, but
also uses the information of the document set itself to expand the query in
various ways (mean expansion, variance expansion and TextRank expansion).
Compared with the previous query expansion methods, our method combines
multiple query expansion methods to better represent query information, and at
the same time, it makes a useful attempt on manifold ranking. In addition, we
use the degree of word overlap and the proximity between words to calculate the
similarity between sentences. We performed experiments on the datasets of DUC
2006 and DUC2007, and the evaluation results show that the proposed query
expansion method can significantly improve the system performance and make our
system comparable to the state-of-the-art systems.

Information Retrieval;Artificial Intelligence;Machine Learning