A Search Engine for Discovery of Scientific Challenges and Directions
This addresses the challenge of rapid knowledge discovery for researchers, particularly in biomedicine and interdisciplinary fields like COVID-19 research, though it is incremental as it builds on existing search and extraction methods.
The authors tackled the problem of researchers struggling to discover important scientific challenges and directions due to an overwhelming flood of papers, by developing a search engine for extracting and searching these elements, which outperformed a popular scientific search engine in experiments with 19 researchers and clinicians.
Keeping track of scientific challenges, advances and emerging directions is a fundamental part of research. However, researchers face a flood of papers that hinders discovery of important knowledge. In biomedicine, this directly impacts human lives. To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery. We construct and release an expert-annotated corpus of texts sampled from full-length papers, labeled with novel semantic categories that generalize across many types of challenges and directions. We focus on a large corpus of interdisciplinary work relating to the COVID-19 pandemic, ranging from biomedicine to areas such as AI and economics. We apply a model trained on our data to identify challenges and directions across the corpus and build a dedicated search engine. In experiments with 19 researchers and clinicians using our system, we outperform a popular scientific search engine in assisting knowledge discovery. Finally, we show that models trained on our resource generalize to the wider biomedical domain and to AI papers, highlighting its broad utility. We make our data, model and search engine publicly available. https://challenges.apps.allenai.org/