CBAG: Conditional biomedical abstract generation.

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...

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Autores principales:	Justin Sybrandt, Ilya Safro
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/74f0df3208804bc1b0c9d625465a318b
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spelling	oai:doaj.org-article:74f0df3208804bc1b0c9d625465a318b2021-12-02T20:15:37ZCBAG: Conditional biomedical abstract generation.1932-620310.1371/journal.pone.0253905https://doaj.org/article/74f0df3208804bc1b0c9d625465a318b2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253905https://doaj.org/toc/1932-6203Biomedical 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.Justin SybrandtIlya SafroPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0253905 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Justin Sybrandt Ilya Safro CBAG: Conditional biomedical abstract generation.
description	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.
format	article
author	Justin Sybrandt Ilya Safro
author_facet	Justin Sybrandt Ilya Safro
author_sort	Justin Sybrandt
title	CBAG: Conditional biomedical abstract generation.
title_short	CBAG: Conditional biomedical abstract generation.
title_full	CBAG: Conditional biomedical abstract generation.
title_fullStr	CBAG: Conditional biomedical abstract generation.
title_full_unstemmed	CBAG: Conditional biomedical abstract generation.
title_sort	cbag: conditional biomedical abstract generation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/74f0df3208804bc1b0c9d625465a318b
work_keys_str_mv	AT justinsybrandt cbagconditionalbiomedicalabstractgeneration AT ilyasafro cbagconditionalbiomedicalabstractgeneration
_version_	1718374533877989376

CBAG: Conditional biomedical abstract generation.

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