Automating scholarly article data collection with Action Science Explorer

摘要

Keeping up with rapidly emerging research fronts in various inter-disciplinary fields requires significant effort from scholars and researchers. These users are concerned not only with finding relevant articles or websites, but also for gaining the understanding of key articles, authors, citation information, and current trends. Several tools such as Action Science Explorer (ASE) have been developed to evaluate the network of citations between articles, recognize important papers and their clusters, summarize them automatically, delve into the full-text of papers to fetch context, generate reviews, create annotations and finally export results in numerous document authoring formats. Although ASE is useful for researchers and scholars, as a research prototype it is limited and tested on data from the ACL Anthology Network. ASE does not have the ability to automatically import and process scholarly articles from external repositories such as Google Scholar, IEEE Xplore, and the ACM Digital Library. This paper contributes an enhanced ASE which automates the data import process: starting with a web search, then generating a citation network, statistics, text analytics, and cluster summaries. Our enhanced ASE gives researchers in many fields the ability to gain an understanding of their academic literature: the key papers, authors, research fronts, hypothesis, and state of the art.