Summarization from Medical Documents: A Survey <s> Extractive single-document summarization <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> BIB001 </s> Summarization from Medical Documents: A Survey <s> Extractive single-document summarization <s> A BSTRACT : Medical information is available from a variety of new online resources. Given the number and diversity of sources, methods must be found that will enable users to quickly assimilate and determine the content of a document. Summarization is one such tool that can help users to quickly determine the main points of a document. Previous methods to automatically summarize text documents typically do not attempt to infer or define the content of a document. Rather these systems rely on secondary features or clues that may point to content. This paper describes text summarization techniques that enable users to focus on the key content of a document. The techniques presented here analyze groups of similar documents in order to form a content model. The content model is used to select sentences forming the summary. The technique does not require additional knowledge sources; thus the method should be applicable to any set of text documents. <s> BIB002 </s> Summarization from Medical Documents: A Survey <s> Extractive single-document summarization <s> Despite the large amount of online medical literature, it can be difficult for clinicians to find relevant information at the point of patient care. In this paper, we present techniques to personalize the results of search, making use of the online patient record as a sophisticated, pre-existing user model. Our work in PERSIVAL, a medical digital library, includes methods for re-ranking the results of search to prioritize those that better match the patient record. It also generates summaries of the re-ranked results which highlight information that is relevant to the patient under the physician's care. We focus on the use of a common representation for the articles returned by search and the patient record which facilitates both the re-ranking and the summarization tasks. This common approach to both tasks has a strong positive effect on the ability to personalize information. <s> BIB003 </s> Summarization from Medical Documents: A Survey <s> Extractive single-document summarization <s> The ScandSum research network (ScandSum 2003) has helped coordinate Nordic research on summarization, especially for the Scandinavian languages. Such a research effort was badly needed, since at present there is a lack of usable tools for summarization targeted at those languages. In today’s information society, the overflow of textual information, especially on the Internet and increasingly delivered through mobile devices, makes summarization useful and sometimes indispensable. The current state of the art is primarily based on extraction techniques which attempt to identify and retain the most relevant sentences in a text. The resulting extract should ideally contain enough information to satisfy the user’s needs and at the same time it should not contain any redundant or superfluous information. The user s needs can roughly be described as either indicative (topic of the text) or informative (central information in the text). Spotting keywords and named entities in a text are useful for attaining these goals. A further requirement for a summary is that it should ideally be a fluent text without any gaps that could be misleading to the reader. In particular, missing antecedents of anaphors may cause problems. Therefore, anaphor resolution techniques may help to increase the quality of summaries. It is difficult to formulate what makes an ideal summary. Therefore any effort at automatic summarization must carefully assess user s needs. Research will clearly benefit from an analysis of human summarization and a comparative evaluation of machine generated summaries. Further reading about the research field can be found in (Mani & Maybury 1999) <s> BIB004 </s> Summarization from Medical Documents: A Survey <s> Extractive single-document summarization <s> This summarization approach exploits the results of recent progress in information extraction to represent salient units of text and their relationships. By exploiting meaningful relations between units and the perspective from which the comparison is desired, the summarizer can pinpoint similarities and differences, generate composite summaries, and align text segments. These techniques have also been evaluated. <s> BIB005
One of the projects belonging in this category is MiTAP BIB001 . The aim of MiTAP (MITRE Text and Audio Processing) BIB004 is to monitor infectious disease outbreaks or other biological threats by monitoring multiple information sources such as epidemiological reports, newswire feeds, email, online news, television news and radio news in multiple languages. All the captured information is filtered and the resulting information is normalized. Each normalized article is passed through a zoner that uses human-created rules to identify the source, date, and other fields such as the article title and body. The zoned messages are processed to identify paragraph, sentence and word boundaries as well as part of-speech tags. The processed messages are then fed into a named entity recognizer, which identifies person, organization and location names as well as dates, diseases, and victim descriptions using human-created rules. Finally, the document is processed by WebSumm BIB005 , which generates a summary out of modified versions of extracted sentences. For non-English sources, a machine translation system is used to translate the messages automatically into English. In addition to single-document summarization, MiTAP has recently incorporated two types of multidocument summarization: Newsblaster 14 BIB003 automatically clusters articles and generates summaries based on each cluster, Alias I 15 produces summaries on particular entities and generates daily top ten lists of diseases in the news. Another project is MUSI  . MUSI stands for "MUltilingual Summarization for the Internet" and it is a cross-lingual summarization system, which uses articles from The Journal of Anaesthesiology as input. The journal is freely accessible online  and its articles are written in Italian and English. MUSI takes those articles and creates summaries from them in French and German. The system is query-based and it extracts sentences from the input article according to the following criteria: cue phrases, position of the sentences, query words and compression rate. That is, MUSI follows the Edmundsonian paradigm for the selection of the sentences. Once the sentences have been extracted, two approaches can be followed: either they are used as they are to form the extractive summary or they are converted into a semantic representation to produce an abstractive summary. A third project exploiting extractive techniques is presented in BIB002 . The most important aspect of this approach is that it ranks the extracted sentences according to the so-called cluster signature of the document. More specifically, their prototype system takes medical documents (result of a query using a search engine) as input and clusters them into groups. These groups are then analyzed for features with high support, called key features, forming a cluster signature that best characterizes each document group. The summary is generated by matching the cluster signature to each sentence of the document to be summarized. Both the sentence and the cluster signature are represented using a vector space model. The ranked sentences are then selected and presented to the user as a summary. BIB002 used for their experiments abstracts and full texts from the Journal of the American Medical Association.