The contribution of cause-effect link to representing the core of scientific paper-The role of Semantic Link Network.

Mengyun Cao,Xiaoping Sun,Hai Zhuge

doi:10.1371/journal.pone.0199303

Abstract

The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing.

Highlights

Text is a kind of representation that conveys idea
(3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated
The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing

Summary

Introduction

The distributions of the annotated cause-effect links within each paper of the OBSERVATION dataset are given in S4 Appendix, confirming that sections with higher Cover Rate reflect the higher intensity of representing cause-effect links. To unveil the impact of the cause-effect link on summarization, we proposed four schemes for incorporating the annotated cause-effect links or the auto-extracted ones into nine benchmark summarization models to improve the quality of extractive summarization Each of these benchmark models more or less uses some is-part-of link or similar link to build the instances of Semantic Link Network among language units (such as words, sentences, paragraphs and sections), uses these Semantic Link Network instances to determine the ranks of sentences, and extracts higher ranked sentences as automatically generated summary for each paper. We posted the source codes of the experiments of this paper in GitHub [53]

Related works

Findings

Conclusion