The Covid-19 pandemic has caused a spur in the medical research literature. With new research advances in understanding the virus, there is a need for robust text mining tools which can process, extract and present answers from the literature in a concise and consumable way. With a DialoGPT based multi-turn conversation generation module, and BM-25 \& neural embeddings based ensemble information retrieval module, in this paper we present a conversational system, which can retrieve and answer coronavirus-related queries from the rich medical literature, and present it in a conversational setting with the user. We further perform experiments to compare neural embedding-based document retrieval and the traditional BM25 retrieval algorithm and report the results.
@article{arxiv.2108.01436,
title = {Medical Literature Mining and Retrieval in a Conversational Setting},
author = {Souvik Das and Sougata Saha and Rohini K. Srihari},
journal= {arXiv preprint arXiv:2108.01436},
year = {2021}
}