CBAG: Conditional biomedical abstract generation

Justin Sybrandt and Ilya Safro

PLOS ONE, 2021, vol. 16, issue 7, 1-18

Abstract: Biomedical research papers often combine disjoint concepts in novel ways, such as when describing a newly discovered relationship between an understudied gene with an important disease. These concepts are often explicitly encoded as metadata keywords, such as the author-provided terms included with many documents in the MEDLINE database. While substantial recent work has addressed the problem of text generation in a more general context, applications, such as scientific writing assistants, or hypothesis generation systems, could benefit from the capacity to select the specific set of concepts that underpin a generated biomedical text. We propose a conditional language model following the transformer architecture. This model uses the “encoder stack” to encode concepts that a user wishes to discuss in the generated text. The “decoder stack” then follows the masked self-attention pattern to perform text generation, using both prior tokens as well as the encoded condition. We demonstrate that this approach provides significant control, while still producing reasonable biomedical text.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0253905

DOI: 10.1371/journal.pone.0253905

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