Information Retrieval and Extraction on COVID-19 Clinical Articles Using Graph Community Detection and Bio-BERT Embeddings (2020)

Abstract: In this paper, we present an information retrieval system on a corpus of scientific articles related to COVID-19. We build a similarity network on the articles where similarity is determined via shared citations and biological domain-specific sentence embeddings. Ego-splitting community detection on the article network is employed to cluster the articles and then the queries are matched with the clusters. Extractive summarization using BERT and PageRank methods is used to provide responses to the query. We also provide a Question-Answer bot on a small set of intents to demonstrate the efficacy of our model for an information extraction module.

## methodology we briefly describe our method here. 1. network of the articles: we build a citation graph of the articles in the corpus where nodes corresponds to the papers and the edges are determined by the citations of the papers. if two papers have a common citation, then we add an edge between them. in addition, we include edges between papers if their semantic similarity is greater than a threshold. w use biosentvec @xcite which is trained on a corpus of about 30 million clinical and bio-medical research articles from the public databases -pubmed and mimic-iii. biosentvec provides 700-dimensional sentence embeddings.