CBAG: Conditional Biomedical Abstract Generation
This work addresses the need for domain-specific language models in biomedical research to support applications like writing assistants, but it is incremental as it builds on existing transformer architectures.
The authors tackled the problem of generating biomedical abstracts, which use specialized language that reduces the effectiveness of general NLP models, by proposing a transformer-based conditional language model that generates abstracts from titles, publication years, and keywords, and demonstrated it outperforms GPT-2 in producing relevant entities.
Biomedical research papers use significantly different language and jargon when compared to typical English text, which reduces the utility of pre-trained NLP models in this domain. Meanwhile Medline, a database of biomedical abstracts, introduces nearly a million new documents per-year. Applications that could benefit from understanding this wealth of publicly available information, such as scientific writing assistants, chat-bots, or descriptive hypothesis generation systems, require new domain-centered approaches. A conditional language model, one that learns the probability of words given some a priori criteria, is a fundamental building block in many such applications. We propose a transformer-based conditional language model with a shallow encoder "condition" stack, and a deep "language model" stack of multi-headed attention blocks. The condition stack encodes metadata used to alter the output probability distribution of the language model stack. We sample this distribution in order to generate biomedical abstracts given only a proposed title, an intended publication year, and a set of keywords. Using typical natural language generation metrics, we demonstrate that this proposed approach is more capable of producing non-trivial relevant entities within the abstract body than the 1.5B parameter GPT-2 language model.