Automated text summarisation and evidence-based medicine: A survey of two domains <s> Progress in Medical Summarisation and Question <s> Abstract Objectives: To characterise the information needs of family doctors by collecting the questions they asked about patient care during consultations and to classify these in ways that would be useful to developers of knowledge bases. Design: Observational study in which investigators visited doctors for two half days and collected their questions. Taxonomies were developed to characterise the clinical topic and generic type of information sought for each question. Setting: Eastern Iowa. Participants: Random sample of 103 family doctors. Main outcome measures: Number of questions posed, pursued, and answered; topic and generic type of information sought for each question; time spent pursuing answers; information resources used. Results: Participants asked a total of 1101 questions. Questions about drug prescribing, obstetrics and gynaecology, and adult infectious disease were most common and comprised 36% of all questions. The taxonomy of generic questions included 69 categories; the three most common types, comprising 24% of all questions, were “What is the cause of symptom X?” “What is the dose of drug X?” and “How should I manage disease or finding X?” Answers to most questions (702, 64%) were not immediately pursued, but, of those pursued, most (318, 80%) were answered Doctors spent an average of less than 2 minutes pursuing an answer, and they used readily available print and human resources Only two questions led to a formal literature search. Conclusions: Family doctors in this study did not pursue answers to most of their questions. Questions about patient care can be organised into a limited number of generic types, which could help guide the efforts of knowledge base developers. Key messages Questions that doctors have about the care of their patients could help guide the content of medical information sources and medical training In this study of US family doctors, participants frequently had questions about patient care but did not pursue answers to most questions (64%) On average, participants spent less than 2 minutes seeking an answer to a question The most common resources used to answer questions included textbooks and colleagues; formal literature searches were rarely performed The most common generic questions were “What is the cause of symptom X?” “What is the dose of drug X?” and “How should I manage disease or finding X?” <s> BIB001 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Progress in Medical Summarisation and Question <s> Automated question answering has been a topic of research and development since the earliest AI applications. Computing power has increased since the first such systems were developed, and the general methodology has changed from the use of hand-encoded knowledge bases about simple domains to the use of text collections as the main knowledge source over more complex domains. Still, many research issues remain. The focus of this article is on the use of restricted domains for automated question answering. The article contains a historical perspective on question answering over restricted domains and an overview of the current methods and applications used in restricted domains. A main characteristic of question answering in restricted domains is the integration of domain-specific information that is either developed for question answering or that has been developed for other purposes. We explore the main methods developed to leverage this domain-specific information. <s> BIB002 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Progress in Medical Summarisation and Question <s> We propose an approach to automatic clinical question answering based on UMLS relations. Rules are defined to identify the medical concepts and their relations in the questions and documents with the help of Metamap transfer and SemRep. The phrase-level answers are generated through matching concepts and relations between question and documents. Experiment shows that our approach obtains high precision and recall. <s> BIB003 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Progress in Medical Summarisation and Question <s> Automatically extracting information needs from ad hoc clinical questions is an important step towards medical question answering. In this work, we first explored supervised machine-learning approaches to automatically classify an ad hoc clinical question into general topics. We then explored both unsupervised and supervised methods for automatically extracting keywords from an ad hoc clinical question. Our methods were evaluated on the 4,654 clinical questions maintained by the National Library of Medicine. Our best systems or methods showed F-score of 76% for the task of question-general topic classification and of 58% for extracting keywords from ad hoc clinical questions. <s> BIB004 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Progress in Medical Summarisation and Question <s> Restricted domains such as medicine set a context where question-answering is more likely expected to be associated with knowledge and reasoning (Molla and Vicedo, 2007; Ferret and Zweigenbaum, 2007). On the one hand, knowledge and reasoning may be more necessary than in open-domain question-answering because of more specific or more difficult questions. On the other hand, it may also be more manageable, since by definition restricted-domain QA should not have to face the same breadth of questions as open-domain QA. It is therefore interesting to study the role of knowledge and reasoning in restricted-domain question-answering systems. We shall do so in the case of the (bio-)medical domain, which has a long tradition of investigating knowledge representation and reasoning and, more generally, artificial intelligence methods (Shortliffe et al., 1975), and which has seen a growing interest in question-answering systems (Zweigenbaum, 2003; Yu et al., 2005; Demner-Fushman and Lin, 2007; Zweigenbaum et al., 2007). <s> BIB005 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Progress in Medical Summarisation and Question <s> Objectives: In this survey, we reviewed the current state of the art in biomedical QA (Question Answering), within a broader framework of semantic knowledge-based QA approaches, and projected directions for the future research development in this critical area of intersection between Artificial Intelligence, Information Retrieval, and Biomedical Informatics. Materials and methods: We devised a conceptual framework within which to categorize current QA approaches. In particular, we used ''semantic knowledge-based QA'' as a category under which to subsume QA techniques and approaches, both corpus-based and knowledge base (KB)-based, that utilize semantic knowledge-informed techniques in the QA process, and we further classified those approaches into three subcategories: (1) semantics-based, (2) inference-based, and (3) logic-based. Based on the framework, we first conducted a survey of open-domain or non-biomedical-domain QA approaches that belong to each of the three subcategories. We then conducted an in-depth review of biomedical QA, by first noting the characteristics of, and resources available for, biomedical QA and then reviewing medical QA approaches and biological QA approaches, in turn. The research articles reviewed in this paper were found and selected through online searches. Results: Our review suggested the following tasks ahead for the future research development in this area: (1) Construction of domain-specific typology and taxonomy of questions (biological QA), (2) Development of more sophisticated techniques for natural language (NL) question analysis and classification, (3) Development of effective methods for answer generation from potentially conflicting evidences, (4) More extensive and integrated utilization of semantic knowledge throughout the QA process, and (5) Incorporation of logic and reasoning mechanisms for answer inference. Conclusion: Corresponding to the growth of biomedical information, there is a growing need for QA systems that can help users better utilize the ever-accumulating information. Continued research toward development of more sophisticated techniques for processing NL text, for utilizing semantic knowledge, and for incorporating logic and reasoning mechanisms, will lead to more useful QA systems. <s> BIB006
Answering. In recent years, medical document summarisation has received significant research attention, which attempt to make use of greater computational power and the availability of more sources of domain knowledge. A variety of document types are now addressed by the different approaches, and QA systems have been developed specific to this domain. Most work on query-focused summarisation in this research area has been executed under the broader domain of QA. While early NLP focused on domain-independent QA, restricted domain QA has received focus over the last decade BIB002 . Restricted domain QA, particularly in the medical domain, require incorporation of terminological and specialised domain-specific knowledge  BIB005 . Some research has focused primarily on question analysis and the coarse-grained classification as the first step for medical QA BIB006 . , and BIB004 , for example, study the answerability of clinical questions and attempt to classify clinical questions based on the Evidence Taxonomy BIB001 , and also into general topics. The QA approach proposed by BIB003 relies heavily on semantic information present on the questions and documents. First, candidate sentences are identified and phrases are extracted by identifying mappings between question and answer semantic types. The system has been evaluated for factoid and complex questions and is shown to have very good recall and precision (77% and 92% respectively). The use of medical semantic types to identify salient information in sentences based on query needs have been exploited in later, more comprehensive summarisation systems, which we discuss in the next subsection. Finally,  present a dynamic summarisation approach using an algorithm called Combo to identify salient semantic predications. It was shown to outperform several baseline methods in terms of recall and precision.