Amplifying scientific paper's abstract by leveraging data-weighted reconstruction. Issue 4 (July 2016)

• Record Type: Journal Article
• Authors: Yang, Shansong; Lu, Weiming; Zhang, Zhanjiang; Wei, Baogang; An, Wenjia
• Is Part Of: Information processing & management. Volume 52:Issue 4(2016:Jul.)
• Page Start: 698
• Page End: 719
• Publication Date: 2016-07
• Subjects: Document summarization -- Citation analysis -- Scientific literature -- Data-weighted reconstruction; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2015.12.014
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: This paper explores the impact of heterogeneous bibliographic network for generating scientific paper's amplified abstract. The amplified abstract is generated by leveraging target scientific paper's abstract and citation sentence's content and structure, which is addressed through document summarization manner. Sentence's weight is learned by exploiting regularization for ranking on heterogeneous bibliographic network. Data-weighted reconstruction is proposed to assign different priority to sentences when reconstructing the original document. Various evaluation metrics are designed to validate the effectiveness of our approach. Abstract: In this paper, we focus on the problem of automatically generating amplified scientific paper's abstract which represents the most influential aspects of scientific paper. The influential aspects can be illustrated by the target scientific paper's abstract and citation sentences discussing the target paper, which are provided in papers citing the target paper. In this paper, we extract representative sentences through data-weighted reconstruction approach(DWR) by jointly leveraging target scientific paper's abstract and citation sentences' content and structure. In our study, we make two-folded contributions. Firstly, sentence's weight was learned by exploiting regularization for ranking on heterogeneous bibliographic network. Specially, Sentences-similar-Sentences relationship was identified by language modeling-based approach and added to the bibliographic network. Secondly, a data-weighted reconstruction objective function is optimized to select the most representative sentences which reconstructs the original sentence set with minimum error. In this process, sentences' weight plays a critical role. Experimental evaluation over real dataset confirms the effectiveness of our approach.