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.
PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization
School of Engineering, Westlake University（西湖大学工程学院）；Amazon AWS AI（亚马逊AWS人工智能部门）；Sichuan University（四川大学）；Shanghai AI Lab（上海人工智能实验室）；New York University Shanghai（上海纽约大学）；Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖高等研究院先进技术研究所）
University of Pittsburgh（匹兹堡大学）；McGill University（麦吉尔大学）；Microsoft Research（微软研究院）
Faceted summarization provides briefings of a document from different perspectives. Readers can quickly comprehend the main points of a long document with the help of a structured outline. However, little research has been conducted on this subject, partially due to the lack of large-scale faceted summarization datasets. In this study, we present FacetSum, a faceted summarization benchmark built on Emerald journal articles, covering a diverse range of domains. Different from traditional document-summary pairs, FacetSum provides multiple summaries, each targeted at specific sections of a long d…
We introduce iFacetSum, a web application for exploring topical document sets. iFacetSum integrates interactive summarization together with faceted search, by providing a novel faceted navigation scheme that yields abstractive summaries for the user's selections. This approach offers both a comprehensive overview as well as concise details regarding subtopics of choice. Fine-grained facets are automatically produced based on cross-document coreference pipelines, rendering generic concepts, entities and statements surfacing in the source texts. We analyze the effectiveness of our application th…
Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, National Institutes of Health（美国国立卫生研究院美国国家医学图书馆利斯特希尔生物医学通信中心）
Automatic summarization of natural language is a widely studied area in computer science, one that is broadly applicable to anyone who routinely needs to understand large quantities of information. For example, in the medical domain, recent developments in deep learning approaches to automatic summarization have the potential to make health information more easily accessible to patients and consumers. However, to evaluate the quality of automatically generated summaries of health information, gold-standard, human generated summaries are required. Using answers provided by the National Library …
Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
Duke–NUS Medical School（杜克-新加坡国立大学医学院）；University of Cambridge（剑桥大学）；The University of Tokyo（东京大学）；Singapore General Hospital（新加坡中央医院）；University of Geneva（日内瓦大学）；Duke School of Medicine（杜克大学医学院）；Duke Clinical Research Institute（杜克临床研究所）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Center（新加坡国家眼科中心）；Stanford University（斯坦福大学）；Singapore Health Services（新加坡保健服务集团）；National University of Singapore（新加坡国立大学）
Northwestern University Feinberg School of Medicine（西北大学范伯格医学院）
Background: Clinical guidelines and recommendations are the driving wheels of the evidence-based medicine (EBM) paradigm, but these are available primarily as unstructured text and are generally highly heterogeneous in nature. This significantly reduces the dissemination and automatic application of these recommendations at the point of care. A comprehensive structured representation of these recommendations is highly beneficial in this regard. Objective: The objective of this paper to present Clinical Recommendation Type System (CRTS), a common type system that can effectively represent a cli…
Objective: The aim of this paper is to survey the recent work in medical documents summarization. Background: During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc. Methodology: This survey g…
CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation
Department of Computer Science, University of Copenhagen（哥本哈根大学计算机科学系）；Center for Information and Language Processing (CIS), LMU Munich（慕尼黑大学信息与语言处理中心）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Text summarization in medicine can help doctors for reducing the time to access important information from countless documents. The paper offers a supervised extractive summarization method based on conditional generative adversarial networks using convolutional neural networks. Unlike previous models, which often use greedy methods to select sentences, we use a new approach for selecting sentences. Moreover, we provide a network for biomedical word embedding, which improves summarization. An essential contribution of the paper is introducing a new loss function for the discriminator, making t…
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Massachusetts Institute of Technology（麻省理工学院）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；Institute for AI in Medicine, University Hospital Essen（埃森大学医院医学人工智能研究所）；Georgia Institute of Technology（佐治亚理工学院）；Northeastern University（东北大学）
The rapid growth of scientific literature has made it difficult for the researchers to quickly learn about the developments in their respective fields. Scientific document summarization addresses this challenge by providing summaries of the important contributions of scientific papers. We present a framework for scientific summarization which takes advantage of the citations and the scientific discourse structure. Citation texts often lack the evidence and context to support the content of the cited paper and are even sometimes inaccurate. We first address the problem of inaccuracy of the cita…
Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison
Stanford University Palo Alto CA USA（斯坦福大学）；Department of Neurology Mayo Clinic Rochester MN USA（梅奥诊所神经科）；Department of Neurology Dalhousie University Halifax Canada（达尔豪斯大学神经科）；Jefferson Headache Center Department of Neurology Thomas Jefferson University PA USA（泰勒大学神经科）；Beth Israel Deaconess Medical Center Boston MA USA（贝斯以色列医疗中心）；Department of Neurology University of Florida Gainesville FL USA（佛罗里达大学神经科）；University of Colorado School of Medicine Department of Pediatrics Division of Child Neurology Aurora CO USA（科罗拉多医学院儿科部儿童神经科）；Department of Medicine Mount Sinai Hospital Icahn School of Medicine at Mount Sinai New York NY USA（西奈医院医学部）；Department of Neurology Mayo Clinic Scottsdale AZ USA（梅奥诊所Scottsdale分部）；Harvard Medical School Boston MA USA（哈佛医学院）；Department of Neurology Mount Sinai Hospital Icahn School of Medicine at Mount Sinai New York NY USA（西奈医院神经科）
