Automated text summarisation and evidence-based medicine: A survey of two domains <s> SUMMARISATION AND QUESTION ANSWERING FOR THE MEDICAL DOMAIN <s> 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 gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics. Discussion and conclusions:: The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications. <s> BIB001 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> SUMMARISATION AND QUESTION ANSWERING FOR THE MEDICAL DOMAIN <s> As the number of electronic biomedical textual resources increases, it becomes harder for physicians to find useful answers at the point of care. Information retrieval applications provide access to databases; however, little research has been done on using automatic summarization to help navigate the documents returned by these systems. After presenting a semantic abstraction automatic summarization system for MEDLINE citations, we concentrate on evaluating its ability to identify useful drug interventions for 53 diseases. The evaluation methodology uses existing sources of evidence-based medicine as surrogates for a physician-annotated reference standard. Mean average precision (MAP) and a clinical usefulness score developed for this study were computed as performance metrics. The automatic summarization system significantly outperformed the baseline in both metrics. The MAP gain was 0.17 (p<0.01) and the increase in the overall score of clinical usefulness was 0.39 (p<0.05). <s> BIB002
In this section, we provide an overview of text summarisation and Question Answering (QA) approaches targeted towards the medical domain. The goal here is to present some of the important research work in this area and analytically review more recent and promising approaches. The medical domain itself is quite broad, and so we attempt to adhere to research work that is relevant for EBM. It must be mentioned that, in the recent past, there has been steady ongoing research in biomedical and medical text processing BIB001  ]. However, compared to other domains, there is very little published research in summarisation and question answering BIB002 . A variety of tools and resources have also been made available to aid summarisation approaches in this domain.