Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including attributes of each panel and arrangements of graphical elements are learned and inferred from data. During the inference stage, an MAP inference framework is employed to incorporate some design principles. In order to bridge the gap between panel attributes and the composition within each panel, we also propose a recursive page splitting algorithm to generate the panel layout for a poster. To learn and validate our model, we collect and release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.
The formalization of the architecture of documents and text formatting are the central issues of this paper. Besides a fundamental and theoretical approach toward these topics, an overview is presented of the COBATEF system. The COBATEF system is a context-based text formatting system, for which a software, as well as a hardware, implementation is available. A unique feature of the system is its automatic text-element recognition mechanism, which is context based and consequently takes advantage of the implicit structure of text. A predefined layout for each type of text element then opens the way for a fully automatic text-processing system in which user control information can be reduced to an absolute minimum. Grid-based page designs are ubiquitous in commercially printed publications, such as newspapers and magazines. Yet, to date, no one has invented a good way to easily and automatically adapt such designs to arbitrarily-sized electronic displays. The difficulty of generalizing grid-based designs explains the generally inferior nature of on-screen layouts when compared to their printed counterparts, and is arguably one of the greatest remaining impediments to creating on-line reading experiences that rival those of ink on paper. In this work, we present a new approach to adaptive grid-based document layout, which attempts to bridge this gap. In our approach, an adaptive layout style is encoded as a set of grid-based templates that know how to adapt to a range of page sizes and other viewing conditions. These templates include various types of layout elements (such as text, figures, etc.) and define, through constraint-based relationships, just how these elements are to be laid out together as a function of both the properties of the content itself, such as a figure's size and aspect ratio, and the properties of the viewing conditions under which the content is being displayed. We describe an XML-based representation for our templates and content, which maintains a clean separation between the two. We also describe the various parts of our research prototype system: a layout engine for formatting the page; a paginator for determining a globally optimal allocation of content amongst the pages, as well as an optimal pairing of templates with content; and a graphical user interface for interactively creating adaptive templates. We also provide numerous examples demonstrating the capabilities of this prototype, including this paper, itself, which has been laid out with our system. We review the literature on automatic document formatting with an emphasis on recent work in the field. One common way to frame document formatting is as a constrained optimization problem where decision variables encode element placement, constraints enforce required geometric relationships, and the objective function measures layout quality. We present existing research using this framework, describing the kind of optimization problem being solved and the basic optimization techniques used to solve it. Our review focuses on the formatting of primarily textual documents, including both micro- and macro-typographic concerns. We also cover techniques for automatic table layout. Related problems such as widget and diagram layout, as well as temporal layout issues that arise in multimedia documents are outside the scope of this review. This paper discusses a new approach to the problem of dividing the text of a paragraph into lines of approximately equal length. Instead of simply making decisions one line at a time, the method considers the paragraph as a whole, so that the final appearance of a given line might be influenced by the text on succeeding lines. A system based on three simple primitive concepts called ‘boxes’, ‘glue’, and ‘penalties’ provides the ability to deal satisfactorily with a wide variety of typesetting problems in a unified framework, using a single algorithm that determines optimum breakpoints. The algorithm avoids backtracking by a judicious use of the techniques of dynamic programming. Extensive computational experience confirms that the approach is both efficient and effective in producing high-quality output. The paper concludes with a brief history of line-breaking methods, and an appendix presents a simplified algorithm that requires comparatively few resources. We present a new paradigm for automated document composition based on a generative, unified probabilistic document model (PDM) that models document composition. The model formally incorporates key design variables such as content pagination, relative arrangement possibilities for page elements and possible page edits. These design choices are modeled jointly as coupled random variables (a Bayesian Network) with uncertainty modeled by their probability distributions. The overall joint probability distribution for the network assigns higher probability to good design choices. Given this model, we show that the general document layout problem can be reduced to probabilistic inference over the Bayesian network. We show that the inference task may be accomplished efficiently, scaling linearly with the content in the best case. We provide a useful specialization of the general model and use it to illustrate the advantages of soft probabilistic encodings over hard one-way constraints in specifying design aesthetics. Layout refers to the process of determining the sizes and positions of the visual objects that are part of an information presentation. Automated layout refers to the use of a computer program to automate either all or part of the layout process. This field of research lies at the crossroads between artificial intelligence and human computer interaction. Automated layout of presentations is becoming increasingly important as the amount of data that we need to present rapidly overtakes our ability to present it manually. We survey and analyze the techniques used by research systems that have automated layout components and suggest possible areas of future work.