Ontology-based and User-focused Automatic Text Summarization (OATS): Using COVID-19 Risk Factors as an Example
This system aims to help the medical community efficiently review scientific literature by focusing on user-specified information, such as COVID-19 risk factors.
This paper introduces OATS, an Ontology-based and user-focused Automatic Text Summarization system designed to extract sentences from unstructured text that align with a user's specific interests. It was demonstrated using COVID-19 risk factors to help the medical community identify and review relevant scientific literature.
This paper proposes a novel Ontology-based and user-focused Automatic Text Summarization (OATS) system, in the setting where the goal is to automatically generate text summarization from unstructured text by extracting sentences containing the information that aligns to the user's focus. OATS consists of two modules: ontology-based topic identification and user-focused text summarization; it first utilizes an ontology-based approach to identify relevant documents to user's interest, and then takes advantage of the answers extracted from a question answering model using questions specified from users for the generation of text summarization. To support the fight against the COVID-19 pandemic, we used COVID-19 risk factors as an example to demonstrate the proposed OATS system with the aim of helping the medical community accurately identify relevant scientific literature and efficiently review the information that addresses risk factors related to COVID-19.