IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications
Qi Li, Yuyang Ren, Xingli Wang, Luoyi Fu, Jiaxin Ding, Xinde Cao,, Xinbing Wang, Chenghu Zhou

TL;DR
IdeaReader is a system that automatically identifies influential papers and summarizes the flow of ideas in scientific literature, aiding researchers in understanding how ideas evolve and influence each other.
Contribution
It introduces a novel machine reading approach that clusters references and citations to map idea flow and generates automatic literature reviews.
Findings
Successfully clusters references and citations to reveal idea flow.
Automatically generates summaries of influential papers.
Provides visualizations of idea evolution across publications.
Abstract
Understanding the origin and influence of the publication's idea is critical to conducting scientific research. However, the proliferation of scientific publications makes it difficult for researchers to sort out the evolution of all relevant literature. To this end, we present IdeaReader, a machine reading system that finds out which papers are most likely to inspire or be influenced by the target publication and summarizes the ideas of these papers in natural language. Specifically, IdeaReader first clusters the references and citations (first-order or higher-order) of the target publication, and the obtained clusters are regarded as the topics that inspire or are influenced by the target publication. It then picks out the important papers from each cluster to extract the skeleton of the idea flow. Finally, IdeaReader automatically generates a literature review of the important papers…
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Taxonomy
TopicsScientific Computing and Data Management · Expert finding and Q&A systems · Complex Network Analysis Techniques
