Can language models read biomedical texts and explain the biomedical
mechanisms discussed? In this work we introduce a biomedical mechanism
summarization task. Biomedical studies often investigate the mechanisms behind
how one entity (e.g., a protein or a chemical) affects another in a biological
context. The abstracts of these publications often include a focused set of
sentences that present relevant supporting statements regarding such
relationships, associated experimental evidence, and a concluding sentence that
summarizes the mechanism underlying the relationship. We leverage this
structure and create a summarization task, where the input is a collection of
sentences and the main entities in an abstract, and the output includes the
relationship and a sentence that summarizes the mechanism. Using a small amount
of manually labeled mechanism sentences, we train a mechanism sentence
classifier to filter a large biomedical abstract collection and create a
summarization dataset with 22k instances. We also introduce conclusion sentence
generation as a pretraining task with 611k instances. We benchmark the
performance of large bio-domain language models. We find that while the
pretraining task help improves performance, the best model produces acceptable
mechanism outputs in only 32% of the instances, which shows the task presents
significant challenges in biomedical language understanding and summarization.

本文介绍了生物医学机制总结任务，并利用少量手工标注的机制句子，训练了机制句子分类器以过滤大量的生物医学摘要并创建了一个包含 22k 实例的总结数据集。此外，我们还引入了 611k 实例的结论句子生成作为预训练任务，并基准测试了大型生物领域语言模型的性能。我们发现，虽然预训练任务有助于提高性能，但最佳模型仅在 32％的情况下产生可接受的机制输出，这表明该任务在生物医学语言理解和总结方面面临巨大挑战。