  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.

Digital Libraries;Computation and Language