Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization.
Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization.
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DOI：
10.18653/v1/2020.findings-emnlp.304
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发表时间：
2020-11
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影响因子：
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通讯作者：
Kavuluru R
中科院分区：
文献类型：
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作者：
Noh J;Kavuluru R
精准医疗（PM）的信息检索（IR）通常涉及寻找表征患者病例的多个证据。这通常至少包括病症的名称和适用于患者的遗传变异。其他因素，如人口统计学属性、合并症和社会决定因素也可能相关。因此，检索问题通常被公式化为ad hoc搜索，但具有多个方面（例如，疾病，突变），可能需要纳入。在本文中，我们提出了一个文档重排序的方法，结合神经查询文档匹配和文本摘要对这样的检索场景。我们的架构建立在基本的BERT模型上，有三个特定的组件用于重新排序：（a）。文档-查询匹配（B）。关键词提取和（c）.分面条件抽象概括。（B）和（c）的结果用于基本上将候选文档转换成简明摘要，该简明摘要可以与手头的查询进行比较以计算相关性分数。组件（a）直接生成查询的候选文档的匹配分数。完整的架构受益于文档查询匹配的互补潜力和新的文档转换方法的基础上总结沿着PM方面。使用NIST的TREC-PM跟踪数据集（2017-2019）进行的评估表明，我们的模型达到了最先进的性能。为了促进可重复性，我们的代码可以在这里获得：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.