A Study on Structural and Semantic Analysis for Presentation Content Management

兵庫県立大学大学院201

Analysing features of lecture slides and past exam paper materials towards automatic associating E-materials for self-revision

Digital materials not only provide opportunities as enablers of e-learning development, but also create a new challenge. The current e-materials provided on a course website are individually designed for learning in classrooms rather than for revision. In order to enable the capability of e-materials to support a students revision, we need an efficient system to associate related pieces of different e-materials. In this case, the features of each item of e-material, including the structure and the technical terms they contain, need to be studied and applied in order to calculate the similarity between relevant e-materials. Even though difficulties regarding technical term extraction and the similarities between two text documents have been widely discussed, empirical experiments for particular types of e-learning materials (for instance, lecture slides and past exam papers) are still rare. In this paper, we propose a framework and relatedness model for associating lecture slides and past exam paper materials to support revision based on Natural Language Processing (NLP) techniques. We compare and evaluate the efficiency of different combinations of three weighted schemes, term frequency (TF), inverse document frequency (IDF), and term location (TL), for calculating the relatedness score. The experiments were conducted on 30 lectures (~900 slides) and 3 past exam papers (12 pages) of a data structures course at the authors’ institution. The findings indicate the appropriate features for calculating the relatedness score between lecture slides and past exam papers

Summarization from Medical Documents: A Survey

Objective: The aim of this paper is to survey the recent work in medical documents summarization. Background: During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc. Methodology: This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics. Discussion and conclusions: The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applicationsComment: 21 pages, 4 table

Video summarisation: A conceptual framework and survey of the state of the art

This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2007 Elsevier Inc.Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. This paper presents a conceptual framework for video summarisation derived from the research literature and used as a means for surveying the research literature. The framework distinguishes between video summarisation techniques (the methods used to process content from a source video stream to achieve a summarisation of that stream) and video summaries (outputs of video summarisation techniques). Video summarisation techniques are considered within three broad categories: internal (analyse information sourced directly from the video stream), external (analyse information not sourced directly from the video stream) and hybrid (analyse a combination of internal and external information). Video summaries are considered as a function of the type of content they are derived from (object, event, perception or feature based) and the functionality offered to the user for their consumption (interactive or static, personalised or generic). It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users

Spartan Daily, October 12, 2017

Volume 149, Issue 22https://scholarworks.sjsu.edu/spartan_daily_2017/1063/thumbnail.jp

Video Abstracting at a Semantical Level

One the most common form of a video abstract is the movie trailer. Contemporary movie trailers share a common structure across genres which allows for an automatic generation and also reflects the corresponding moviea s composition. In this thesis a system for the automatic generation of trailers is presented. In addition to action trailers, the system is able to deal with further genres such as Horror and comedy trailers, which were first manually analyzed in order to identify their basic structures. To simplify the modeling of trailers and the abstract generation itself a new video abstracting application was developed. This application is capable of performing all steps of the abstract generation automatically and allows for previews and manual optimizations. Based on this system, new abstracting models for horror and comedy trailers were created and the corresponding trailers have been automatically generated using the new abstracting models. In an evaluation the automatic trailers were compared to the original Trailers and showed a similar structure. However, the automatically generated trailers still do not exhibit the full perfection of the Hollywood originals as they lack intentional storylines across shots

Utilization of multimodal interaction signals for automatic summarisation of academic presentations

Multimedia archives are expanding rapidly. For these, there exists a shortage of retrieval and summarisation techniques for accessing and browsing content where the main information exists in the audio stream. This thesis describes an investigation into the development of novel feature extraction and summarisation techniques for audio-visual recordings of academic presentations. We report on the development of a multimodal dataset of academic presentations. This dataset is labelled by human annotators to the concepts of presentation ratings, audience engagement levels, speaker emphasis, and audience comprehension. We investigate the automatic classification of speaker ratings and audience engagement by extracting audio-visual features from video of the presenter and audience and training classifiers to predict speaker ratings and engagement levels. Following this, we investigate automatic identi�cation of areas of emphasised speech. By analysing all human annotated areas of emphasised speech, minimum speech pitch and gesticulation are identified as indicating emphasised speech when occurring together. Investigations are conducted into the speaker's potential to be comprehended by the audience. Following crowdsourced annotation of comprehension levels during academic presentations, a set of audio-visual features considered most likely to affect comprehension levels are extracted. Classifiers are trained on these features and comprehension levels could be predicted over a 7-class scale to an accuracy of 49%, and over a binary distribution to an accuracy of 85%. Presentation summaries are built by segmenting speech transcripts into phrases, and using keywords extracted from the transcripts in conjunction with extracted paralinguistic features. Highest ranking segments are then extracted to build presentation summaries. Summaries are evaluated by performing eye-tracking experiments as participants watch presentation videos. Participants were found to be consistently more engaged for presentation summaries than for full presentations. Summaries were also found to contain a higher concentration of new information than full presentations

Multisource Data Integration in Remote Sensing

Papers presented at the workshop on Multisource Data Integration in Remote Sensing are compiled. The full text of these papers is included. New instruments and new sensors are discussed that can provide us with a large variety of new views of the real world. This huge amount of data has to be combined and integrated in a (computer-) model of this world. Multiple sources may give complimentary views of the world - consistent observations from different (and independent) data sources support each other and increase their credibility, while contradictions may be caused by noise, errors during processing, or misinterpretations, and can be identified as such. As a consequence, integration results are very reliable and represent a valid source of information for any geographical information system

Mapping Scholarly Communication Infrastructure: A Bibliographic Scan of Digital Scholarly Communication Infrastructure

This bibliography scan covers a lot of ground. In it, I have attempted to capture relevant recent literature across the whole of the digital scholarly communications infrastructure. I have used that literature to identify significant projects and then document them with descriptions and basic information. Structurally, this review has three parts. In the first, I begin with a diagram showing the way the projects reviewed fit into the research workflow; then I cover a number of topics and functional areas related to digital scholarly communication. I make no attempt to be comprehensive, especially regarding the technical literature; rather, I have tried to identify major articles and reports, particularly those addressing the library community. The second part of this review is a list of projects or programs arranged by broad functional categories. The third part lists individual projects and the organizations—both commercial and nonprofit—that support them. I have identified 206 projects. Of these, 139 are nonprofit and 67 are commercial. There are 17 organizations that support multiple projects, and six of these—Artefactual Systems, Atypon/Wiley, Clarivate Analytics, Digital Science, Elsevier, and MDPI—are commercial. The remaining 11—Center for Open Science, Collaborative Knowledge Foundation (Coko), LYRASIS/DuraSpace, Educopia Institute, Internet Archive, JISC, OCLC, OpenAIRE, Open Access Button, Our Research (formerly Impactstory), and the Public Knowledge Project—are nonprofit.Andrew W. Mellon Foundatio