Text mining approaches for dealing with the rapidly expanding literature on COVID-19

Abstract

More than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific productivity leads to information overload, making it difficult for researchers, clinicians and public health officials to keep up with the latest findings. Automated text mining techniques for searching, reading and summarizing papers are helpful for addressing information overload. In this review, we describe the many resources that have been introduced to support text mining applications over the COVID-19 literature; specifically, we discuss the corpora, modeling resources, systems and shared tasks that have been introduced for COVID-19. We compile a list of 39 systems that provide functionality such as search, discovery, visualization and summarization over the COVID-19 literature. For each system, we provide a qualitative description and assessment of the system’s performance, unique data or user interface features and modeling decisions. Many systems focus on search and discovery, though several systems provide novel features, such as the ability to summarize findings over multiple documents or linking between scientific articles and clinical trials. We also describe the public corpora, models and shared tasks that have been introduced to help reduce repeated effort among community members; some of these resources (especially shared tasks) can provide a basis for comparing the performance of different systems. Finally, we summarize promising results and open challenges for text mining the COVID-19 literature.

The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System

“…Therefore, many systems based on information retrieval or text mining were created in response to this challenge, including our COVID-SEE Scientific Evidence Explorer system [ 59 ]; more are reviewed in [ 60 61 62 ]. An important resource in these efforts was the COVID-19 Open Research Dataset (CORD-19) which compiled a significant collection of literature for both COVID-19 and related coronaviruses into a single, downloadable resource [ 63 ].…”

Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

“…In addition to clinical and hospital information, researchers investigate scientific literature looking for instance for drug repurposing recommendations [ 52 ], and for temporal evolution of research work on COVID-19 [ 53 ]. Besides, the first systematic reviews related to COVID are proposed [ 54 ], including the focus on research needs [ 55 ].…”

Brief Description of COVID-SEE: The Scientific Evidence Explorer for COVID-19 Related Research

“…In information retrieval and natural language processing, efforts have concentrated on building tools for efficiently managing the growing literature on COVID-19 [21]. While many tools emerged for article retrieval and question answering, relatively few systems go beyond returning a list of (relevant) documents, or leverage domain knowledge to organise and present information found within the literature [35]. Building on observations about the importance of exploratory search [25], with COVID-SEE (Scientific Evidence Explorer), we aim to fill this gap.…”