# CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization

arXiv: https://arxiv.org/abs/2006.09595 · HTML: https://mlsanity.com/paper/2006.09595/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 11 of 11)

- [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021)
- [Modeling Multi-hop Question Answering as Single Sequence Prediction](https://mlsanity.com/paper/2205.09226/) (2022)
- [COVID-19 Named Entity Recognition for Vietnamese](https://mlsanity.com/paper/2104.03879/) (2021)
- [Entity-driven Fact-aware Abstractive Summarization of Biomedical Literature](https://mlsanity.com/paper/2203.15959/) (2022)
- [Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users](https://mlsanity.com/paper/2403.18667/) (2024)
- [SetCSE: Set Operations using Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2404.17606/) (2024)
- [COVIDRead: A Large-scale Question Answering Dataset on COVID-19](https://mlsanity.com/paper/2110.09321/) (2021)
- [Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articles](https://mlsanity.com/paper/2410.04202/) (2024)
- [STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management](https://mlsanity.com/paper/2104.01600/) (2021)
- [Interpretable Self-supervised Multi-task Learning for COVID-19 Information Retrieval and Extraction](https://mlsanity.com/paper/2106.08252/) (2021)
- [An Information Retrieval and Extraction Tool for Covid-19 Related Papers](https://mlsanity.com/paper/2401.16430/) (2024)

## Abstract (arXiv)

The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines. As of May 2020, 128,000 coronavirus-related publications have been collected through the COVID-19 Open Research Dataset Challenge. Here we present CO-Search, a retriever-ranker semantic search engine designed to handle complex queries over the COVID-19 literature, potentially aiding overburdened health workers in finding scientific answers during a time of crisis. The retriever is built from a Siamese-BERT encoder that is linearly composed with a TF-IDF vectorizer, and reciprocal-rank fused with a BM25 vectorizer. The ranker is composed of a multi-hop question-answering module, that together with a multi-paragraph abstractive summarizer adjust retriever scores. To account for the domain-specific and relatively limited dataset, we generate a bipartite graph of document paragraphs and citations, creating 1.3 million (citation title, paragraph) tuples for training the encoder. We evaluate our system on the data of the TREC-COVID information retrieval challenge. CO-Search obtains top performance on the datasets of the first and second rounds, across several key metrics: normalized discounted cumulative gain, precision, mean average precision, and binary preference.