Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of Medical Text Summarisation Approaches <s> From the Publisher: ::: Text summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user or task.. "Until now there has been no state-of-the-art collection of the most important writings in automatic text summarization. This book presents the key developments in the field in an integrated framework and suggests future research areas. The book is organized into six sections. Classical Approaches, Corpus-Based Approaches, Exploiting Discourse Structure, Knowledge-Rich Approaches, Evaluation Methods, and New Summarization Problem Areas. <s> BIB001 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of Medical Text Summarisation Approaches <s> In this paper we show how two standard outputs from information extraction (IE) systems -- named entity annotations and scenario templates -- can be used to enhance access to text collections via a standard text browser. We describe how this information is used in a prototype system designed to support information workers' access to a pharmaceutical news archive as part of their "industry watch" function. We also report results of a preliminary, qualitative user evaluation of the system, which while broadly positive indicates further work needs to be done on the interface to make users aware of the increased potential of IE-enhanced text browsers. <s> BIB002 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of Medical Text Summarisation Approaches <s> The end users of medical digital libraries need quick access to information that is specific to the patients under their care. We present a summarization system that finds and extracts results from multiple medical journal articles returned by a search, filters results that match the patient and merges and orders the remaining facts for the summary. Our approach features an integration of text categorization, information extraction, information fusion and text reformulation for the summarization task. <s> BIB003 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of Medical Text Summarisation Approaches <s> Abstract medSyn D iKATe is a natural language processor, which automatically acquires medical information from findings reports. In the course of text analysis their contents is transferred to conceptual representation structures, which constitute a corresponding text knowledge base. medSyn D iKATe is particularly adapted to deal properly with text structures, such as various forms of anaphoric reference relations spanning several sentences. The strong demands medSyn D iKATe poses on the availability of expressive knowledge sources are accounted for by two alternative approaches to acquire medical domain knowledge (semi)automatically. We also present data for the information extraction performance of medSyn D iKATe in terms of the semantic interpretation of three major syntactic patterns in medical documents. <s> BIB004 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of Medical Text Summarisation Approaches <s> The MiTAP (MITRE Text and Audio Processing) system [13] is a prototype for monitoring infectious disease outbreaks and other global threats. MiTAP focuses on providing timely, multi-lingual, global information access to analysts, medical experts and individuals involved in humanitarian assistance and relief work. Multiple information sources in multiple languages are automatically captured, filtered, translated, summarized, and categorized into searchable newsgroups. Critical information is automatically extracted and tagged to facilitate browsing, searching, and sorting. ::: ::: The demonstration highlights how different users cull different kinds of information from the MiTAP system. Some use it in order to track disease outbreak; others have been using it to monitor economic and political situations worldwide. <s> BIB005 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of Medical Text Summarisation Approaches <s> This paper presents an innovative unsupervised method for automatic sentence extraction using graph-based ranking algorithms. We evaluate the method in the context of a text summarization task, and show that the results obtained compare favorably with previously published results on established benchmarks. <s> BIB006 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of Medical Text Summarisation Approaches <s> Knowing the polarity of clinical outcomes is important in answering questions posed by clinicians in patient treatment. We treat analysis of this information as a classification problem. Natural language processing and machine learning techniques are applied to detect four possibilities in medical text: no outcome, positive outcome, negative outcome, and neutral outcome. A supervised learning method is used to perform the classification at the sentence level. Five feature sets are constructed: unigrams, bigrams, change phrases, negations, and categories. The performance of different combinations of feature sets is compared. The results show that generalization using the category information in the domain knowledge base Unified Medical Language System is effective in the task. The effect of context information is significant. Combining linguistic features and domain knowledge leads to the highest accuracy. <s> BIB007 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of Medical Text Summarisation Approaches <s> BackgroundThe text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research study on airways disease.MethodsThe principal diagnosis, co-morbidity and smoking status extracted by HITEx from a set of 150 discharge summaries were compared to an expert-generated gold standard.ResultsThe accuracy of HITEx was 82% for principal diagnosis, 87% for co-morbidity, and 90% for smoking status extraction, when cases labeled "Insufficient Data" by the gold standard were excluded.ConclusionWe consider the results promising, given the complexity of the discharge summaries and the extraction tasks. <s> BIB008 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of Medical Text Summarisation Approaches <s> Physicians have many questions when caring for patients, and frequently need to seek answers for their questions. Information retrieval systems (e.g., PubMed) typically return a list of documents in response to a user’s query. Frequently the number of returned documents is large and makes physicians’ information seeking “practical only ‘after hours’ and not in the clinical settings”. Question answering techniques are based on automatically analyzing thousands of electronic documents to generate short-text answers in response to clinical questions that are posed by physicians. The authors address physicians’ information needs and described the design, implementation, and evaluation of the medical question answering system (MedQA). Although our long term goal is to enable MedQA to answer all types of medical questions, currently, we currently implement MedQA to integrate information retrieval, extraction, and summarization techniques to automatically generate paragraph-level text for definitional questions (i.e., “What is X?”). MedQA can be accessed at http://www.dbmi.columbia.edu/~yuh9001/research/MedQA.html. <s> BIB009 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of Medical Text Summarisation Approaches <s> Multiple pieces of text describing various pieces of evidence in clinical trials are often needed in answering a clinical question. We explore a multi-document summarization approach to automatically find this information for questions about effects of using a medication to treat a disease. Sentences in relevant documents are ranked according to various features by a machine-learning approach. Those with higher scores are more important and will be included in the summary. The presence of clinical outcomes and their polarity are incorporated into the approach as features for determining importance of sentences, and the effectiveness of this is investigated, along with that of other textual features. The results show that information on clinical outcomes improves the performance of summarization. <s> BIB010 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of Medical Text Summarisation Approaches <s> Text summarization is a method for data reduction. The use of text summarization enables users to reduce the amount of text that must be read while still assimilating the core information. The data reduction offered by text summarization is particularly useful in the biomedical domain, where physicians must continuously find clinical trial study information to incorporate into their patient treatment efforts. Such efforts are often hampered by the high-volume of publications. This paper presents two independent methods (BioChain and FreqDist) for identifying salient sentences in biomedical texts using concepts derived from domain-specific resources. Our semantic-based method (BioChain) is effective at identifying thematic sentences, while our frequency-distribution method (FreqDist) removes information redundancy. The two methods are then combined to form a hybrid method (ChainFreq). An evaluation of each method is performed using the ROUGE system to compare system-generated summaries against a set of manually-generated summaries. The BioChain and FreqDist methods outperform some common summarization systems, while the ChainFreq method improves upon the base approaches. Our work shows that the best performance is achieved when the two methods are combined. The paper also presents a brief physician's evaluation of three randomly-selected papers from an evaluation corpus to show that the author's abstract does not always reflect the entire contents of the full-text. <s> BIB011 </s> Automated text summarisation and evidence-based medicine: A survey of two domains <s> Overview of Medical Text Summarisation Approaches <s> Medication information is one of the most important types of clinical data in electronic medical records. It is critical for healthcare safety and quality, as well as for clinical research that uses electronic medical record data. However, medication data are often recorded in clinical notes as free-text. As such, they are not accessible to other computerized applications that rely on coded data. We describe a new natural language processing system (MedEx), which extracts medication information from clinical notes. MedEx was initially developed using discharge summaries. An evaluation using a data set of 50 discharge summaries showed it performed well on identifying not only drug names (F-measure 93.2%), but also signature information, such as strength, route, and frequency, with F-measures of 94.5%, 93.9%, and 96.0% respectively. We then applied MedEx unchanged to outpatient clinic visit notes. It performed similarly with F-measures over 90% on a set of 25 clinic visit notes. <s> BIB012
4.3.1 Information Extraction Approaches. Early summarisation systems were mostly concerned with extracting relevant information from structured or unstructured medical text. The Linguistic String Project (LSP) [Sager et al. 1994 ] is an example of early medical NLP work. Its primary purpose is the transformation of clinical narratives into formal representations. MedLEE  ] is also responsible for extracting information from clinical narratives and presenting the information in structured forms through the use of a controlled vocabulary. It is used for processing various forms of notes and reports and is integrated with a clinical information system. HiTEx BIB008 ] is yet another system used for extracting findings such as diagnoses and family history from clinical narratives through the use of NLP techniques. TRESTLE (Text Retrieval Extraction and summarisation Technologies for Large Enterprises) BIB002 ] is an information extraction system that generates summarised information from pharmaceutical newsletters in one sentence through the identification of Named Entities (NEs) followed by sentence extraction based on the presence of key NEs. Drug and disease names are considered by this system to be named entities. BIB004 present MEDSYNDIKATE, a natural language processor that automatically extracts clinical information from reports. The contents of the texts are transferred to conceptual representations that correspond to a knowledge base, and the system incorporates domain knowledge to semantically interpret major syntactic patterns in medical documents. BIB012 propose the MedEx system which uses NLP to extract medication information from clinical notes with very high recall and precision. Extractive Summarisation Approaches. Most summarisation systems in this domain applied extractive summarisation approaches like the initial MiTAP system (MITRE Text and Audio Processing) BIB005 which was targeted towards the monitoring of infectious disease outbreaks or other biological threats. MiTAP monitors various sources of information such as online news, television news, and newswire feeds. and captures information which are filtered and processed to identify sentences, paragraphs, and POS tags. The final summary is generated by WebSumm BIB001 as extracted sentences from the processed text. BIB011 propose a single-document, extractive summarisation approach that combines BioChain ], which identifies relevant sentences using concept-chaining (similar to lexical chaining but applied to UMLS concepts), and the FreqDist system ], which uses a frequency distribution model to identify relevant sentences. This hybrid approach, called ChainFreq, sequentially employs the BioChain method to identify candidate sentences that contain relevant concepts, and then the FreqDist method, which generates a set of summary sentences from the chosen sentences. Other extractive approaches have been proposed with various intents: BIB006 presents an approach for automated sentence Non-extractive Approaches. MUSI is an early system that applies semantic information thoroughly  (MUltilingual summarisation for the Internet). It follows an approach similar to the ones already mentioned for sentence extraction but also has the capability of producing semantic representations of the extracted sentences to produce an abstractive summary. Other than the MUSI system, early research in this area did not consider abstractive summarisation. Recently, however, a number of abstractive summarisation systems have been proposed (e.g., PERSIVAL BIB003 , MedQA BIB009 ], EpoCare BIB007 BIB010 . We discuss some of these approaches in more detail in the following subsections.