TY - GEN
T1 - Generating impact-based summaries for scientific literature
AU - Mei, Qiaozhu
AU - Zhai, Cheng Xiang
PY - 2008
Y1 - 2008
N2 - In this paper, we present a study of a novel summarization problem, i.e., summarizing the impact of a scientific publication. Given a paper and its citation context, we study how to extract sentences that can represent the most influential content of the paper. We propose language modeling methods for solving this problem, and study how to incorporate features such as authority and proximity to accurately estimate the impact language model. Experiment results on a SIGIR publication collection show that the proposed methods are effective for generating impact-based summaries.
AB - In this paper, we present a study of a novel summarization problem, i.e., summarizing the impact of a scientific publication. Given a paper and its citation context, we study how to extract sentences that can represent the most influential content of the paper. We propose language modeling methods for solving this problem, and study how to incorporate features such as authority and proximity to accurately estimate the impact language model. Experiment results on a SIGIR publication collection show that the proposed methods are effective for generating impact-based summaries.
UR - http://www.scopus.com/inward/record.url?scp=80052125218&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052125218&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:80052125218
SN - 9781932432046
T3 - ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
SP - 816
EP - 824
BT - ACL-08
T2 - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT
Y2 - 15 June 2008 through 20 June 2008
ER -