Professional to Plain Language Neural Translation: A Path Toward Actionable Health Information

健康素养是做出明智的健康决策以改善结果的关键。然而，虽然同行- 综述的临床文献包含指导健康决策的有价值的信息，通常是为 医疗保健专业人士的观众。即使在良好的一般文化背景下，医学术语和 专业语言的复杂结构使这些信息特别难以解释。尽管工作 我已经做了一些总结，这些文献在平原语言，使其访问的一般 公共，这些努力依赖于人类的专业知识。这种方法无法扩展到匹配的快速步伐， 新的发现出现在文献中。因此，迫切需要自动化的方法来增强 一般公众可获得规范的生物医学文献。这个问题可以被定义为 医疗保健专业人员的语言与医疗保健专业人员的语言之间的翻译问题 消费者拟议的研究建立在神经序列深度学习的最新进展基础上， 序列模型，最初在机器翻译任务中进行评估。在最近的工作中，我们 表明这些模型可以有效地适用于科克伦中摘要之间的翻译任务 系统性综述数据库（CDSR）和相应的专业撰写的简明语言 摘要。由此产生的自动生成的摘要在其 与专业撰写的摘要保持一致。此外，在一次试点用户评价中， 不知道摘要出处，他们通常被认为是他们的专家撰写的。 同行在拟议的研究中，我们将进一步发展这一研究路线，通过评估 额外的预训练和辅助微调任务，作为提高生成摘要质量的一种手段。 我们亦会采用新颖的模式，为有关模型作出特别设计，以提高其事实准确性和可读性。 辅助培训目标和后处理程序。我们将评估我们的方法， 在以系统为中心的内容评估中， 和事实的正确性使用Mechanical Turk，我们将进行以用户为中心的评估， 与CDSR专家编写的普通摘要相比， 语言摘要。这些评估将考虑感知的可解释性和实际的理解， 后者使用多项选择题来评估，以探索理解，回忆和学习。在 这样做，所提出的研究将推进自动简化和摘要的最新技术 生物医学文献供公众消费。

Health literacy is key to making well-informed health decisions that improve outcomes. However, while the peer- reviewed clinical literature contains valuable information to guide health decisions, it is generally written for an audience of healthcare professionals. Even in the context of good general literacy, medical jargon and the complex structure of professional language make this information especially hard to interpret. While efforts have been made to summarize some of this literature in plain language to make it accessible to the general public, these efforts depend on human expertise. This approach cannot scale to match the rapid pace at which new findings emerge in the literature. Thus, there is an urgent unmet need for automated methods to enhance the accessibility of the canonical biomedical literature to the general public. This problem can be framed as a type of translation problem, between the language of healthcare professionals, and that of healthcare consumers. The proposed research builds on recent advances in deep learning stemming from neural sequence- to-sequence models, which were originally evaluated in machine translation tasks. In our recent work, we showed these models can be effectively adapted to the task of translating between abstracts in the Cochrane Database of Systematic Reviews (CDSR) and corresponding professionally-authored plain language summaries. The resulting automatically-generated summaries outperformed those from other models in their alignment with professionally-authored summaries. Furthermore, in a pilot user evaluation in which participants were blinded as to summary provenance, they were generally judged favorably to their expert-authored counterparts. In the proposed research we will develop this line of research further, by evaluating the utility of additional pre-training and auxiliary fine-tuning tasks as a means to improve the quality of generated summaries. We will also customize the models concerned to enhance their factual accuracy and readability using novel auxiliary training objectives and post-processing procedures. We will evaluate our methods as compared with robust baseline models in system-centric evaluations of content alignment with reference summaries, readability and factual correctness. Using Mechanical Turk, we will conduct user-centric evaluations of the ease with which summaries from best-performing models can be understood, as compared with CDSR expert-authored plain language summaries. These evaluations will consider both perceived interpretability, and actual comprehension, with the latter evaluated using sets of multiple choice questions to probe comprehension, recall and learning. In doing so, the proposed research will advance the state-of-the-art in automated simplification and summarization of the biomedical literature for consumption by the general public.