  Recent years have witnessed the dramatic growth of paper volumes with plenty
of new research papers published every day, especially in the area of computer
science. How to glean papers worth reading from the massive literature to do a
quick survey or keep up with the latest advancement about a specific research
topic has become a challenging task. Existing academic search engines such as
Google Scholar return relevant papers by individually calculating the relevance
between each paper and query. However, such systems usually omit the
prerequisite chains of a research topic and cannot form a meaningful reading
path. In this paper, we introduce a new task named Reading Path Generation
(RPG) which aims at automatically producing a path of papers to read for a
given query. To serve as a research benchmark, we further propose SurveyBank, a
dataset consisting of large quantities of survey papers in the field of
computer science as well as their citation relationships. Each survey paper
contains key phrases extracted from its title and multi-level reading lists
inferred from its references. Furthermore, we propose a
graph-optimization-based approach for reading path generation which takes the
relationship between papers into account. Extensive evaluations demonstrate
that our approach outperforms other baselines. A Real-time Reading Path
Generation System (RePaGer) has been also implemented with our designed model.
To the best of our knowledge, we are the first to target this important
research problem. Our source code of RePaGer system and SurveyBank dataset can
be found on here.
