Using Query Expansion in Manifold Ranking for Query-Oriented Multi-Document Summarization
This work addresses query insufficiency for multi-document summarization tasks, representing an incremental improvement over existing methods.
The paper tackled the problem of insufficient query information in query-oriented multi-document summarization by proposing a query expansion method combined with manifold ranking, resulting in significant performance improvements and making the system comparable to state-of-the-art systems on DUC 2006 and DUC 2007 datasets.
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.