Retrieve-and-Rank End-to-End Summarization of Biomedical Studies

An arduous biomedical task involves condensing evidence derived from multiple interrelated studies, given a context as input, to generate reviews or provide answers autonomously. We named this task context-aware multi-document summarization (CA-MDS). Existing state-of-the-art (SOTA) solutions require truncation of the input due to the high memory demands, resulting in the loss of meaningful content. To address this issue effectively, we propose a novel approach called RAMSES, which employs a retrieve-and-rank technique for end-to-end summarization. The model acquires the ability to (i) index each document by modeling its semantic features, (ii) retrieve the most relevant ones, and (iii) generate a summary via token probability marginalization. To facilitate the evaluation, we introduce a new dataset, FAQSUMC19, which includes the synthesizing of multiple supporting papers to answer questions related to Covid-19. Our experimental findings demonstrate that RAMSES achieves notably superior ROUGE scores compared to state-of-the-art methodologies, including the establishment of a new SOTA for the generation of systematic literature reviews using MS2. Quality observation through human evaluation indicates that our model produces more informative responses than previous leading approaches.