论文标题
通过神经匹配和刻度摘要的精确医学的文献检索
Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization
论文作者
论文摘要
精确医学（PM）的信息检索（IR）通常涉及寻找表征患者病例的多种证据。这通常包括至少适用于患者的疾病的名称和遗传变异。人口属性，合并症和社会决定因素等其他因素也可能是相关的。因此，检索问题通常被提出为临时搜索，但可能需要纳入多个方面（例如疾病，突变）。在本文中，我们提出了一种文档重新依据的方法，该方法将神经查询文档匹配和文本摘要结合在一起，以实现此类检索。我们的体系结构以基本的BERT模型为基础，该模型具有三个用于Reranking的特定组件：（a）。文档Query匹配（B）。关键字提取和（c）。面条条件的抽象摘要。 （b）和（c）的结果基本上将候选文档转换为简洁的摘要，可以将其与手头查询进行比较以计算相关性分数。组件（a）直接生成查询候选文档的匹配分数。完整的体系结构受益于文档Query匹配的互补潜力以及基于PM方面的摘要的新颖文档转换方法。使用NIST的TREC-PM轨道数据集（2017--2019）进行评估表明，我们的模型实现了最先进的性能。为了培养可重复性，我们的代码可在此处提供：https：//github.com/bionlproc/text-summ-for-doc-retrieval。
Information retrieval (IR) for precision medicine (PM) often involves looking for multiple pieces of evidence that characterize a patient case. This typically includes at least the name of a condition and a genetic variation that applies to the patient. Other factors such as demographic attributes, comorbidities, and social determinants may also be pertinent. As such, the retrieval problem is often formulated as ad hoc search but with multiple facets (e.g., disease, mutation) that may need to be incorporated. In this paper, we present a document reranking approach that combines neural query-document matching and text summarization toward such retrieval scenarios. Our architecture builds on the basic BERT model with three specific components for reranking: (a). document-query matching (b). keyword extraction and (c). facet-conditioned abstractive summarization. The outcomes of (b) and (c) are used to essentially transform a candidate document into a concise summary that can be compared with the query at hand to compute a relevance score. Component (a) directly generates a matching score of a candidate document for a query. The full architecture benefits from the complementary potential of document-query matching and the novel document transformation approach based on summarization along PM facets. Evaluations using NIST's TREC-PM track datasets (2017--2019) show that our model achieves state-of-the-art performance. To foster reproducibility, our code is made available here: https://github.com/bionlproc/text-summ-for-doc-retrieval.
