TY - GEN
T1 - Using Query Expansion in Manifold Ranking for Query-Oriented Multi-document Summarization
AU - Jia, Quanye
AU - Liu, Rui
AU - Lin, Jianying
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Manifold ranking
KW - Multi-document summarization
KW - Query expansion
KW - WordNet
UR - https://www.scopus.com/pages/publications/85113467687
U2 - 10.1007/978-3-030-84186-7_7
DO - 10.1007/978-3-030-84186-7_7
M3 - 会议稿件
AN - SCOPUS:85113467687
SN - 9783030841850
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 97
EP - 111
BT - Chinese Computational Linguistics - 20th China National Conference, CCL 2021, Proceedings
A2 - Li, Sheng
A2 - Sun, Maosong
A2 - Liu, Yang
A2 - Wu, Hua
A2 - Kang, Liu
A2 - Che, Wanxiang
A2 - He, Shizhu
A2 - Rao, Gaoqi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th China National Conference on Computational Linguistics, CCL 2021
Y2 - 13 August 2021 through 15 August 2021
ER -