Summarization from Medical Documents: A Survey <s> Abstractive multi-document summarization <s> INTRODUCTION The problem addressed in this paper concerns the automatic identification and extraction of medical terms along with their definitions and modifiers from full text consumer-oriented medical articles. The system, DEFINDER (Definition Finder), uses rule-based techniques. The output of our system can be used in several applications: creation and/or enhancement of on-line terminological resources, summarization and text categorization according to level of expertise, e.g. lay vs. technical. <s> BIB001 </s> Summarization from Medical Documents: A Survey <s> Abstractive multi-document summarization <s> The task of creating indicative summaries that help a searcher decide whether to read a particular document is a difficult task. This paper examines the indicative summarization task from a generation perspective, by first analyzing its required content via published guidelines and corpus analysis. We show how these summaries can be factored into a set of document features, and how an implemented content planner uses the topicality document feature to create indicative multidocument query-based summaries. <s> BIB002 </s> Summarization from Medical Documents: A Survey <s> Abstractive multi-document summarization <s> ABSTRACT In this paper, we propose the use of multido ument summarization as a post-pro essing step in do ument retrieval. We examine the use of the summary as a repla ement to the standard ranked list. The form of the summary is novel be ause it has both informative and indi ate elements, designed to help di erent users perform their tasks better. Our summary uses the do uments' topi al stru ture as a ba kbone for its own stru ture, as it was deemed the most useful do ument feature in our study of a orpus of summaries. <s> BIB003 </s> Summarization from Medical Documents: A Survey <s> Abstractive multi-document summarization <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> BIB004 </s> Summarization from Medical Documents: A Survey <s> Abstractive multi-document summarization <s> This work aims at developing innovative algorithms and tools for summarizing echocardiogram videos. Specifically, we summarize the digital echocardiogram videos by temporally segmenting them into the constituent views and representing each view by the most informative frame. For the segmentation we take advantage of the well-defined spatio-temporal structure of the echocardiogram videos. Two different criteria are used: presence/absence of color and the shape of the region of interest (ROI) in each frame of the video. The change in the ROI is due to different modes of echocardiograms present in one study. The representative frame is defined to be the frame corresponding to the end-diastole of the heart cycle. To locate the end-diastole we track the ECG of each frame to find the exact time the time-marker on the ECG crosses the peak of the R-wave. The corresponding frame is chosen to be the key-frame. The entire echocardiogram video can be summarized into either a static summary, which is a storyboard type of summary and a dynamic summary, which is a concatenation of the selected segments of the echocardiogram video. To the best of our knowledge, this is the first automated system for summarizing the echocardiogram videos based on visual content. <s> BIB005 </s> Summarization from Medical Documents: A Survey <s> Abstractive multi-document summarization <s> 1. SYSTEM TECHNICAL DESCRIPTION The ClassMiner system demonstrates a fully implemented tool for scalable video skimming and summarization. The key technology in the system is the integrated medical video content structure and events mining process, which was presented in a paper at the SIGMOD workshop on Data Mining and Knowledge Discovery [1]. As the system architecture in Fig. 1 indicates, we first apply a general video shot segmentation and key-frame selection scheme to parse the video stream into physical units. Then, the video group detection, scene detection and clustering strategies are executed to mine the video content structure. Various visual and audio feature processing techniques are utilized to detect some semantic cues, such as slides, face and speaker changes, etc. within the video, and these detection results are joined together to mine three types of events (presentation, dialog, clinical operation) from the detected video scenes. Finally, a scalable video skimming and summarization tool is constructed based on the mined video content structure and event information to help the user visualize and access video content. <s> BIB006
Apart from informative extractive multi-document summaries, Centrifuser creates indicative abstractive multi-document summaries, as well, which are used by the PERSIVAL users for searching papers. As noted in section 4.3, the approach of BIB002 BIB003 leads to nodes in the topic trees which match with the query of the user. This, they argue, can be the first phase for Natural Language Generation (NLG). In the next step of NLG, which they call planning, they try to figure out which nodes of the topic trees they will summarize. To achieve this, they determine which nodes are relevant, irrelevant and intricate, based on how deep the nodes are, compared with the query node, i.e. the node that matches the user query. Thus, nodes that are descendants of the query node and are below depth k are considered intricate, above depth k relevant and all the other nodes (i.e. the ones that are not descendants of the query node) are irrelevant. In the final NLG step, realization, the ordered information is converted to text. For a more thorough treatment of Centrifuser, see  . Apart from the abstractive indicative summaries that Centrifuser produces, PERSIVAL produces another type of abstractive summary  BIB004 . These summaries are not concerned with highlighting the similarities and differences among several medical articles, but with the creation of an informative abstractive summary. That summary is tailored according to the preferences of two different types of users: the physician, the patient or her relatives. The system should identify in the documents and extract tuples of the form: (Parameter(s), Finding, Relation). The relations can be any of the following six types: association, prediction, risk, absence of association, absence of prediction and absence of risk. They call those tuples results. BIB004 using empirical methods, i.e. interviews with the physicians, concluded that a summary should fulfill the following qualitative criteria, in relation to the results: • Completeness and accuracy. The results should be complete and accurate, in the sense that all the relevant results and only them should be included. • Repetitions and contradictions. The system should identify repetitions and contradictions among the results. In order to do so, BIB004 have created a representation of the results, which allows them to identify relations such as subsumption and contradiction among the results. • Coherence and cohesion. Coherence for BIB004 is established by "accurate aggregation and ordering of the related results". Cohesion is defined as follows: "two sentences are part of the same paragraph, if and only if they are related." Related are the sentences that present either the same finding, or the same parameter(s). The system described in BIB004 takes input from three different sources: • Patient record. In general, the patient record consists of structured documents, usually in tabular form, and unstructured documents, and sometimes it can be very large. • Journal medical articles. Their system takes as input a vast amount of online articles from medical journals on the field of cardiology. In fact, the articles that are input to the system are the ones that globally match the patient, i.e. the ones that contain information relevant to the patient. • The user query. Although the physician's query is posed in natural language, the system does not try to fully understand the question and give an answer, but instead gives as much information as possible about the question using some of the query keywords. The input articles are first classified automatically into three categories: prognosis, treatment, diagnosis. The next step involves the identification and extraction of the results, i.e. the tuples mentioned above. For this purpose, the authors are exploiting the "rigid", as they call it, structure of the medical articles. This means that they try to locate the Results section and select the sentences that are relevant to the patient. The selected sentences are then passed to the extraction module, that extracts in a template form, the following information: the finding(s), the parameters, the relation, the degree of dependence of the parameters, the article and the sentence it has been extracted from and various other minor information. The templates are filled with the aid of handcrafted patterns. The next step involves the determination of which portions, if any, of the extracted parameters are relevant with the patient record. After that, the resulting templates are merged and ordered. To achieve this, the templates are rendered into an internal "semantic" representation, in the form of a graph. From this graph, they are able to identify repetitions and contradictions. A repetition occurs if two nodes are connected by more than one vertex, and the vertices have "similar" types. What is similar and what is not has been "established" in interviews with physicians. A contradiction occurs in the same situation, but now the vertices have different types. Repetitions and contradictions are used in order to create a more coherent summary. With this method they manage to perform the merging of the templates. For the ordering, they use the following criteria: • Query based: a relation that answers the user query is weighted higher. • Salience based: recitations and contradictions are weighted higher. • Domain based: studies with physicians show that some relation types are more interesting than others. For instance a risk relation is weighted higher than an association relation. • Source based: dependent relations from the same template are presented together. The final step involves the creation of the summary, through NLG techniques. In the final summary, all the medical terms are hyperlinked to their definitions. This is achieved by connecting the system of BIB004 with DEFINDER, a text mining tool for extracting definitions of terms from medical articles (see BIB001 ). BIB005 and BIB006 present systems performing summarization of documents which have multimedia content, echocardiograms and medical videos respectively.