# CBAG: Conditional Biomedical Abstract Generation

arXiv: https://arxiv.org/abs/2002.05637 · HTML: https://mlsanity.com/paper/2002.05637/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [Investigating Large Language Models and Control Mechanisms to Improve Text Readability of Biomedical Abstracts](https://mlsanity.com/paper/2309.13202/) (2023)
- [Large Language Models for Biomedical Text Simplification: Promising But Not There Yet](https://mlsanity.com/paper/2408.03871/) (2024)
- [Biomedical Hypothesis Explainability with Graph-Based Context Retrieval](https://mlsanity.com/paper/2511.05498/) (2025)
- [Divide-Prompt-Refine: a Training-Free, Structure-Aware Framework for Biomedical Abstract Generation](https://mlsanity.com/paper/2605.20628/) (2026)

## Abstract (arXiv)

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.