Summarization from Medical Documents: A Survey <s> Multimedia summarization techniques <s> In this paper we examine tire developed techniques and lessons learned in an operational multimedia exploitation system, Broadcast News Editor (BNE) and Broadcast News Navigator (BNN). BNE captures, analyzes, annotates, segments, summarizes, and stores broadcast news audio, video and textual data within tlte context of multimedia database system BNN provides web based retrieval tools from the multimedia database system. TJle key innovation of this system is the detection and segmentation of story segments from the multimedia broadcast stream. TJtis paper discusses: . TJte utility of using story segments to discover broadcast news stories of interest . Tlte textual, video, and audio cues used for story segment detection . Teclmiques developed for identifying story segments . Details of tlte operational BNE and BNN system BNE and BNN are currently used every evening at MJTRE’s Multimedia Researclz Lab and to this point have automatically processed over 6011 news stories from over 349 broadcasts of CNN Prime News ‘t? <s> BIB001 </s> Summarization from Medical Documents: A Survey <s> Multimedia summarization techniques <s> This paper presents a method for combining ::: query-relevance with information-novelty in the context ::: of text retrieval and summarization. The Maximal ::: Marginal Relevance (MMR) criterion strives to reduce ::: redundancy while maintaining query relevance in ::: re-ranking retrieved documents and in selecting appropriate passages for text summarization. Preliminary results ::: indicate some benefits for MMR diversity ranking ::: in document retrieval and in single document summarization. ::: The latter are borne out by the recent results of the ::: SUMMAC conference in the evaluation of summarization ::: systems. However, the clearest advantage is demonstrated ::: in constructing non-redundant multi-document ::: summaries, where MMR results are clearly superior to ::: non-MMR passage selection. <s> BIB002 </s> Summarization from Medical Documents: A Survey <s> Multimedia summarization techniques <s> While the majority of summarization research so far has focused on written documents (mostly news articles or scientific papers), this thesis addresses for the first time the challenge of automatically summarizing spoken dialogues in a variety of genres and without any restriction on domain. To achieve the goal of spoken dialogue summarization, we implement a system (DiaSumm) using a multi-stage architecture with trainable components which addresses the dialogue-specific issues of summarization and which involves (i) speech disfluency detection and removal, (ii) identification and insertion of sentence boundaries, (iii) identification and linking of question-answer regions, (iv) topical segmentation, and (v) information condensation (ranking of relevant pieces of information with the maximum marginal relevance technique (MMR)). We can also optionally reduce the summary content in an orthogonal dimension by rendering only a subset of the phrases within a relevant sentence (typically, noun phrases). For system development and evaluation, we use a corpus of 23 dialogue excerpts from four different text genres, totalling 80 topical segments, about 47000 words, or about 4 hours of recorded speech: English CallHome (informal, colloquial style), Group Meetings (task oriented, rather informal, colloquial), and dialogue oriented television shows: NewsHour and CrossFire (more formal, potentially partially scripted). The corpus had been manually transcribed and was annotated for topical boundaries and relevant text spans by six human annotators. Further, it was annotated for speech disfluencies and questions and their corresponding answers. We devise a word-based evaluation criterion, relative summary accuracy, which reflects how well the summary captures passages that were placed in man-made summaries by the largest number of annotators. The global evaluation, performed on human transcripts, shows that for the two more informal genres (CallHome and Group Meetings), DiaSumm significantly outperforms a baseline using TF*IDF term weighting with MMR ranking only, while tying with the MMR baseline for the two more formal genres. Furthermore, except for the NewsHour corpus, both the MMR baseline and our DiaSumm system are significantly better than a LEAD baseline (first N words of each segment). Finally, when using speech recognizer output, our system can make successful use of speech recognizer confidence scores to focus on sentences which are more likely to be correctly recognized; thereby, the word error rate in summaries can be reduced significantly while relative summary accuracy improves on average. <s> BIB003 </s> Summarization from Medical Documents: A Survey <s> Multimedia summarization techniques <s> Automatic summarization of open-domain spoken dialogues is a relatively new research area. This article introduces the task and the challenges involved and motivates and presents an approach for obtaining automatic-extract summaries for human transcripts of multiparty dialogues of four different genres, without any restriction on domain. We address the following issues, which are intrinsic to spoken-dialogue summarization and typically can be ignored when summarizing written text such as news wire data: (1) detection and removal of speech disfluencies; (2) detection and insertion of sentence boundaries; and (3) detection and linking of cross-speaker information units (question-answer pairs). A system evaluation is performed using a corpus of 23 dialogue excerpts with an average duration of about 10 minutes, comprising 80 topical segments and about 47,000 words total. The corpus was manually annotated for relevant text spans by six human annotators. The global evaluation shows that for the two more informal genres, our summarization system using dialogue-specific components significantly outperforms two baselines: (1) a maximum-marginal-relevance ranking algorithm using TF*IDF term weighting, and (2) a LEAD baseline that extracts the first n words from a text. <s> BIB004
In this section, work on multimedia summarization is discussed. This involves such diverse fields as dialog summarization, summarization of diagrams, and video summarization. Due to the limited number of relevant works and their field-specific features, only one approach per field is presented. Dialogue summarization: BIB003 BIB004 works on dialogue summarization in unrestricted domains and genres. Zechner's works with transcripts of human dialogs, which are generated either manually or automatically (in this case Zechner also addresses the issue of speech recognition errors). The major steps in Zechner's work are the following. In the first step, input tokenization, all noise is removed and the transcript is tokenized. Disfluency detection follows in which false starts, restarts or repairs and filled pauses are annotated and corrected. In this step the boundaries of the speakers' sentences are also detected. In the next step, cross-speaker information linking, pairs of question-answer among the speakers are identified and annotated. Those pairs are extracted together later when producing the summary, thus making the resulting summary more coherent and informative. The following step is topic segmentation where segments of the discussion on a certain topic are identified and a list of keywords, for each topic, is extracted. The final step, sentence ranking and selection, uses a version of the Maximal Marginal Relevance (MMR) algorithm BIB002 to create a summary of extracted sentences, for each topic. Diagram summarization:  presented a preliminary and only partially implemented work on diagram summarization, yet quite innovative. His aim was to present a summary of the diagrams in a scientific paper, either by selecting (i.e. extracting) one or more diagrams from the paper or by distilling (i.e. simplifying) a diagram, or even by merging several diagrams. Futrelle assumes that a structural description of the diagrams can be obtained as metadata from the author or by parsing the diagrams. In order to achieve his goals he takes into account not only the structural description of each diagram, but the text in its caption or in the diagram itself. Video summarization:  extract information from various media in order to provide a summary of a broadcast news story. They use MITRE's Broadcast News Navigator (BNN; BIB001 ) which helps searching and browsing of news stories, not from just one channel, but from a variety of sources. They use silence and speaker change from the audio, anchor and logo detection from the video and closed-captioned text in order to segment the stream into news stories. For the purpose of presenting a summary, they experiment with a diversity of presentation methods, which include mixed-media. The first one is key-frames, i.e. important single frames and shots i.e. an important sequence of frames. The second one is extracted single sentences, based on weights according to the presence of named entities (organizations, persons and places). The final one is named entities keywords.