Summarization from Medical Documents: A Survey <s> Promising paths for future research <s> Excerpts of technical papers and magazine articles that serve the purposes of conventional abstracts have been created entirely by automatic means. In the exploratory research described, the complete text of an article in machine-readable form is scanned by an IBM 704 data-processing machine and analyzed in accordance with a standard program. Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance, first for individual words and then for sentences. Sentences scoring highest in significance are extracted and printed out to become the "auto-abstract." <s> BIB001 </s> Summarization from Medical Documents: A Survey <s> Promising paths for future research <s> From the Publisher: ::: Until now, most discourse researchers have assumed that full semantic understanding is necessary to derive the discourse structure of texts. This book documents the first serious attempt to construct automatically and use nonsemantic computational structures for text summarization. Daniel Marcu develops a semantics-free theoretical framework that is both general enough to be applicable to naturally occurring texts and concise enough to facilitate an algorithmic approach to discourse analysis. He presents and evaluates two discourse parsing methods: one uses manually written rules that reflect common patterns of usage of cue phrases such as "however" and "in addition to"; the other uses rules that are learned automatically from a corpus of discourse structures. By means of a psycholinguistic experiment, Marcu demonstrates how a discourse-based summarizer identifies the most important parts of texts at levels of performance that are close to those of humans. ::: Marcu also discusses how the automatic derivation of discourse structures may be used to improve the performance of current natural language generation, machine translation, summarization, question answering, and information retrieval systems. <s> BIB002 </s> Summarization from Medical Documents: A Survey <s> Promising paths for future research <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> Summarization from Medical Documents: A Survey <s> Promising paths for future research <s> This chapter reviews research results in the field of Generic User Modeling Systems. It describes the purposes of such systems, their services within user-adaptive systems, and the different design requirements for research prototypes and commercial deployments. It discusses the architectures that have been explored so far, namely shell systems that form part of the application, central server systems that communicate with several applications, and possible future agent-based user modeling systems. Major implemented research prototypes and commercial systems are briefly described. <s> BIB004
Although initial work on Summarization dates back to the late 50's and 60's (e.g. BIB001 4] ), most research on the field has been performed during the last few years. The result is that the research field has not yet achieved a mature state, and a variety of challenges still need to be overcome. The scaling to large size collections of documents, the use of more sophisticated natural language processing techniques for generating abstracts, the availability of annotated summarization corpora for training and testing purposes, are some of these challenges. This is also the case for the domain of medical documents. The study of existing summarization techniques in other domains, the examination of different types of medical documents and the study of techniques reported so far in the literature for medical summarization lead to certain interesting remarks concerning the promising paths for future research. These remarks are presented below in terms of the summarization factors. In terms of the input medium, almost all methods concern summarization from text, although the specific domain can provide a lot of useful input in other media as well (e.g. speech, images, videos). Summarizing information from different media (e.g. spoken transcriptions and textual reports related to the specific echo-videos) is an important issue for practical applications, representing a promising path for future research and development. Concerning the number of the input documents, both categories of techniques (single and multidocument) have been examined. As it is the case in other domains, apart from medicine, single-document summarization methods are mainly using extractive techniques, whereas almost all of the multi-document summarizers are based on abstractive techniques. However, the selection between the simpler extractive techniques and the more complex abstractive ones should not only be based on the number of input documents, but also on the available resources, tools and the summary purpose and output factors. Concerning the language of the input document(s), most of the existing systems are monolingual (English in almost all the cases). There are two cases (MiTAP, MUSI), where the multilingual aspect was taken into account. In the MiTAP case, this was due to the domain (monitoring disease outbreaks) where the information sources are in various languages. On the other hand, MUSI summarizes the articles of a bilingual Journal. In the medical domain there is an enormous amount of documents in various categories (e.g. patient records) in other languages apart from English. There are resources and tools in several other languages that can be exploited in building summarizers for handling more than one language using either shallow or deeper approaches for language processing. In relation to purpose factors, the existing methods mainly concern indicative summarization. The purpose of such summaries is to navigate the reader to the required information, which seems to be sufficient for most practical applications in medicine as long as no better solution is available. The production of indicative summaries seems to "indicate" that the shallow summarizing strategies used so far are not enough for producing informative or even critical summaries. Deeper language processing techniques  and their combination with shallow processing ones seems to be a promising path for future research in NLP in general and more specifically in summarization. There is a trend towards user-oriented summaries, which is reasonable since summarization systems in the medical domain aim to cover the information needs of different user types (clinicians, researchers, patients) and specific users. User involvement does not concern only the submission of a query to the system, but also the summary customization and presentation according to the user's model. The PERSIVAL system BIB003 maintains information about the users' preferences taking into account their expertise in the domain, as well as the users' access tasks. The summary presentation can also be affected by the user's model (e.g. production of a summary in the form of hypertext, combination of text and images or video, etc.). Personalized access to medical information is a crucial issue and needs to be further investigated. There is a lot of expertise from the application of user modeling techniques in other domains which can also be exploited in the medical domain (see BIB004  ). Domain customization is another significant issue. Most of the existing medical summarization systems are able to process documents belonging in specific sub-domains of medicine. Emphasis must be given to the development of technology that can be easily ported to new sub-domains. The development of open architecture systems with reusable and trainable components and resources is imperative in summarization technology. This is directly related to the ability of exploiting pre-existing medical knowledge resources. There are currently various knowledge repositories such as the Unified Medical Language System (UMLS) BIB002 , and MeSH, which can be exploited in several ways by summarization engines. For instance, they can be used to locate interesting document(s), and interesting sentences inside those documents. They can even be used to create conceptual representations of the selected sentences in order to produce abstractive summaries in the same or in a different language. Such approaches are presented in the literature and can be further investigated. The development of customizable summarization technologies requires also indepth study of the medical document types and medical sublanguage. A general-purpose system must be able to exploit the various characteristics of the medical documents. For instance, the sectioning of scientific articles, the specialized language used in emailed reports or in patient records are important features that can significantly affect the performance of the involved language processing tools. In general, the research community must cooperate towards the development of portable summarization technology and the medical domain can provide the necessary application areas. Concerning the output factors, the quality of the summarization output is strongly related to the summarization task. Therefore, qualitative and quantitative criteria need to be established following a study of the domain and the users' interests. In terms of the decision between extractive and abstractive techniques, as noted above, this has to take into account several factors related to the input documents, the purpose of the summary, the qualitative criteria established as well as the available resources and tools.