Using Query Expansion in Manifold Ranking for Query-Oriented Multi-Document Summarization

TL;DR

This paper introduces a novel query expansion method integrated with manifold ranking for query-oriented multi-document summarization, enhancing query representation and improving summary quality based on experiments on DUC datasets.

Contribution

It proposes a combined query expansion approach utilizing WordNet and document set information within manifold ranking, which is a new application in multi-document summarization.

Findings

Significant performance improvement over previous methods

Comparable to state-of-the-art systems on DUC datasets

Effective use of multiple query expansion techniques

Abstract

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.