Summarizing scientific literature on the basis of deconstructed systematic reviews and meta-analyses

Abstract

There is an acute need for large-scale help digesting scientific literature. In 2018, the total number of published scientific articles was estimated at 2.52 million and the number of scientific journals at around 30.000 . With such vast amounts of new information, in addition to the vast amounts that already exist, it is virtually humanly impossible to be fully up to date on the latest developments in most scientific fields. The literature itself, and the information it conveys, is heterogeneous. Studies within the same field of research may well point in different directions (positive, negative, or non-significant) This presents added challenges to developing useful multi-document summaries over the information. Furthermore, studies have shown that a large proportion of the research articles produced at various research institutions around the world are not read by a sufficient large number of people outside a narrow circle of subject-oriented professionals or academics . The valuable knowledge that society spends large amounts of resources on creating the framework for, is thus not utilized to the extent one could wish for, which of course is not optimal for any of the involved parties. To gain a clearer overall picture for the true direction within a research field, it is necessary to synthesize many comparable studies through review techniques and statistical methods in the form of systematic reviews and meta-analyzes. A greater focus on systematic reviews and meta-analyzes would be able to if not rectify the at least alleviate the problems with fuzzy research and low readability rate as the directional arrows within the individual research areas would be much clearer and the amount of literature needed for lay people to read in order to stay fairly up to date within a given research area, would be significantly reduced. The focus of this paper is to is to construct a corpus of articles that, as an example, look at the relationship between Top-management team gender diversity and corporate performance.

Anders McIlquham-Schmidt

PhD fellow

Mateusz researches in neural set-to-sequence models and catalogue/sequence optimisation.

Leon Derczynski

Associate professor

My research interests include NLP for misinformation detection and verification, clinical record processing, online harms, and efficient AI.