PubTrends: a scientific literature explorer

ABSTRACTWith an ever-increasing number of scientific papers published each year, it becomes more difficult for researchers to explore unfamiliar or fast-growing research areas. This greatly inhibits the potential for cross-disciplinary research. When approaching a new subject, a researcher often starts with a search of relevant papers in a dedicated search engine - Google Scholar, Scopus, etc. Besides the search by title, author names, or keywords, these services also provide the user with basic statistics such as number of citations of the paper. However, it is hard to organize the information from these papers, especially with the current rise in publication numbers. For instance, regarding the latest COVID-19 epidemic, thousands of papers were published in the first months. Bibliometrics methods [1] allow for structuring the information in papers by highlighting the most frequent keywords in the selected area. Also, the number of citations is widely used to rank papers according to their estimated scientific impact, citation and co-citation networks for groups of papers may reflect the underlying structure of the research field [2]. Papers are more likely to be co-cited if they belong to closely related research topics, and network analysis allows extracting meaningful clusters representing different scientific directions. Another approach for structuring the information is based on the methods for natural language processing. In particular, we have shown that citation context can be used for summarization of papers and, subsequently, research topics [3]. To our knowledge, no freely available tools exist that combine different approaches for structuring information in research papers in an easy-to-use web service. Most bibliometrics tools require manual preparation of papers dataset, and most of the summarization methods are lacking ready-to-use implementation. We present PubTrends - a scientific publication exploratory tool capable of analyzing the intellectual structure of a research field and similar papers analysis. The service is available at https://pubtrends.net and works with papers from the PubMed [4] database of biomedical texts. Within search results for a given query or a list of paper ids, it shows the most cited papers, frequent keywords, most relevant authors, and journals. We combine bibliometrics methods for citation information analysis and natural language processing algorithms to compute similarity between papers, followed by topics extraction with clustering. Integrated viewer for citation graph and paper similarity network with rich capabilities of visualization and filtering allows for quick navigation through the different aspects of the research field. Finally, we apply deep learning methods to interactively generate automated literature reviews. In addition to implementation details and examples, we demonstrate that topic extraction algorithms produce relevant results by comparison with topics extracted from selected review papers from Nature Reviews journals.