IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications
This addresses the challenge for researchers in managing and understanding the proliferation of scientific publications, though it appears incremental as it builds on existing clustering and summarization techniques.
The authors tackled the problem of tracking idea evolution in scientific literature by developing IdeaReader, a system that identifies influential papers and summarizes idea flow, resulting in automated literature reviews and visualizations.
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 in each topic. Our system can help researchers gain insight into how scientific ideas flow from the target publication's references to citations by the automatically generated survey and the visualization of idea flow.