Abstract:
The present invention provides for analyzing document content for display with reduced cognitive load assists those who are blind, have low vision, or cognitive problems. This present invention also aids those who prefer to receive condensed information orally. Document content is analyzed, a set of salient words and phrases are generated from the document content and the set of salient words and phrases are read.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is a continuation of, and claims the benefit of the filing date of, co-pending U.S. patent application Ser. No. 10/932,725 entitled DOCUMENT CONTENT ANALYSIS TECHNOLOGY FOR REDUCING COGNITIVE LOAD filed Sep. 2, 2004. 
     
    
     FIELD OF THE INVENTION  
       [0002]    The present invention relates generally to accessibility functions for a document on a computer and, more particularly, to a method for analyzing document content that reduces the cognitive load for a user. 
       BACKGROUND 
       [0003]    When reading newspapers, documents, or web pages, there are significant amounts of text in which salient information is imbedded. For people with low vision, dyslexia, blindness, some eye-motor disabilities, and most cognitive deficiencies, text density and organization create challenges. Not only is there a huge cognitive processing demand required, but the individual must parse meaningful information from non-meaningful information without the benefit of additional cueing (e.g., color, chunking, etc). In addition, the blind person using a screen reading device must perform this higher level cognitive function while also listening to the information, making it necessary to retain large quantities of spoken information in working memory buffers to derive the meaning contained within a few key words. 
         [0004]    Typical industry solutions to date utilize a document summarizer. Summarization technology addresses the problem of information overload by reducing a full document to a surrogate summary consisting of a few sentences extracted from the document in a way which retains the essence of the document content. Summarization technology addresses technical challenges like coherence, cohesion, and information quotient. However, even though the information reduction it achieves would help a person with cognitive disabilities, summarization does not explicitly address perceptual problems which might require solutions identifying salient text fragments of granularity smaller than a sentence. 
         [0005]    In particular, none of the summarization technologies listed above ‘tag’ words or phrases with a salience measure. Consequently, these technologies are unable to focus on short text fragments; brevity being of the essence from the point of view of a person with cognitive disabilities. Furthermore, not much attention has been paid to contextualizing the salient fragments. Summarization solutions do not, typically, relate a summary to the original document source, which makes it hard to create a cognitive map between the summary and the full text. 
         [0006]    Therefore, there is a need for a solution to reduce the heavy cognitive load, addressing at least some of the problems associated with conventional document summarizers. 
       SUMMARY OF THE INVENTION 
       [0007]    The present invention provides for Document content analyzing document content so the content can be displayed with reduced cognitive load. The document content is analyzed and a set of salient words and phrases are generated from the document content. A marked-up document containing the set of salient words and phrases are then read. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS  
         [0008]    For a more complete understanding of the present invention, and the advantages thereof, reference is now made to the following Detailed Description taken in conjunction with the accompanying drawings, in which: 
           [0009]      FIG. 1  illustrates a method for analyzing document content and displaying the document content with a reduced cognitive load; 
           [0010]      FIG. 2  illustrates a detailed method of analyzing document content; 
           [0011]      FIG. 3  illustrates a method of generating a set of salient words and phrases; and 
           [0012]      FIG. 4  illustrates a method of reading a salient marked-up document including the salient words and phrases of the salient marked-up document. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0013]    The present invention is described to a large extent in this specification in terms of methods and systems for analyzing document content for display with a reduced cognitive load. However, persons skilled in the art will recognize that a system for operating in accordance with the disclosed methods also falls within the scope of the present invention. The system could be carried out by a computer program or parts of different computer programs. 
         [0014]    This invention can also be embodied in a computer program product, such as a CD-Rom or other recording medium, for use with any suitable data processing system. Persons skilled in the art would recognize that any computer system having suitable programming means will be capable of executing the steps of the method of the invention as embodied in a program product. Although most of the exemplary embodiments described in this specification are oriented to software installed and executing on computer hardware, persons skilled in the art would recognize alternative embodiments implemented as firmware or as hardware are within the scope of the present invention. 
         [0015]    Referring now to  FIG. 1 , the reference numeral  100  generally indicates a system by which document content can be received. A remote computer  154  or a server  152  connected by a network  111  sends document content  190  to a local computer  150 . Also, a local computer can already have access to the document content  190  through internal storage such as a hard drive or RAM, or other stored data such as a CD-Rom. 
         [0016]    A “Network” is used in this detailed description to mean one of any networked pairing for data communications among computers or computer systems. Examples of networks include intranets, extranets, internets, local area networks (LAN), wide area networks (WAN), and other network arrangements as would occur to those persons of skill in the art. 
         [0017]    Referring now to  FIG. 2  the reference numeral  200  generally indicates an exemplary system for document content analysis. In the system  200 , document content  190  is sent to a document content analyzer  192 . After the document content  190  is analyzed by the document content analyzer  192 , a salient word generator  194  creates a set of salient words and phrases  196  from the document content  190 . The set of salient words and phrases  196  can also be included as part of a larger, salient marked-up document. Although not depicted in the exemplary system  200 , a salient word generator can also be included as part of a document content analyzer as would occur to those of skill in the art. A reader  198  capable of reading the set of salient words and phrases  196  can create audible representations by sending the set of salient words and phrases  196  to a text-to-speech engine or display the set of salient words and phrases  196  on a computer screen. The reader  198  not only reads the set of salient words and phrases, but can also read XML tags, HTML, image tags, metatags and so on. 
         [0018]    The salient marked-up document not only includes the set of salient words and phrases but can also include images, tables, charts, and etc. The function of the reader  198  reading images can be suppressed by the system  200 , or the ALT tags on the images can be read aloud. For example, words in that salient marked-up document underlined by the document content analyzer could be read louder, or HTML links could be read in a different voice. The method of  FIG. 1  can read tables, forms, and frames and allows users to search for specific text on a Web page as well as on the entire Web. 
         [0019]    Salient, as used in this specification, means prominent and also includes having a quality that thrusts itself into attention. Salience acts as a mediator for the type and quantity of the information reduction over the original document, defined in terms of small, brief, information-bearing chunks. Salient words and phrases are the words and phrases considered by the document content analysis as bearing information. 
         [0020]    Document content analysis, in a particular configuration, aims to process a text document, and by means of a pipeline of linguistic processing (interchangeably driven by morphosyntactic rules and/or statistical models of human language subsystems), it identifies words and phrases which are potential ‘bearers’ of new information. A morphosyntactic operation is an ordered, dynamic relation between one linguistic form and another. 
         [0021]    Turning now to  FIG. 3 , the reference numeral  300  generally indicates an exemplary method for analyzing document content. The method  300  includes selecting a threshold of prominence  352 . Selecting a threshold of prominence  352  is typically carried out by a user at a computer using computer inputs such as a mouse or a keyboard. Alternatively, the method  300  includes using the default prominence  354  settings. Selecting a threshold of prominence includes selecting prominence settings such as those for identifying, word, sentence, and paragraph boundaries. 
         [0022]    Document content found to fall below the threshold level of prominence does not have to be literally dropped. The document content that falls below the threshold of prominence can remain in the document and remain unmarked or can be deleted from the document completely. 
         [0023]    After either selecting the threshold of prominence  352  or using the default prominence settings  354 , the exemplary method  300  includes identifying salient words and phrases  356 . Identifying salient words and phrases can be carried out by counting names, or by recognizing domain terms, abbreviations and document tags. Identifying salient words and phrases  356  works best with documents in which the major document structure elements are explicitly indicated. Structural elements include but are not limited to titles, headings, headers, footers, tables, lists. Identifying salient words and phrases can also be typically carried out by scanning the text for words and phrases that rise above the selected threshold of prominence. The document structural cues are informative of which sentences are salient by virtue of their position and which portions of text should not be considered. For documents without structural cues, the method  300  can also rely on general word statistics, such as word counts, to determine salience. 
         [0024]    The method  300  also includes contextualizing salient words and phrases  358 . After identifying the salient words and phrases  356 , the method  300  contextualizes salient words and phrases to discover whether or not the identified salient words and phrases make sense. Typically, most word processors contain spell checkers that contextualize salient words and phrases and can be modified by those with skill in the art to carry out contextualizing salient words and phrases in the method  300 . Contextualizing the salient words and phrases  356  also typically involves arranging the identified salient words and phrases and placing them in a certain context. The method  300  determines which order and sense the set of salient words and phrases belong. After contextualizing the salient words and phrases, the method  300  generates the salient words and phrases. 
         [0025]    Generating the salient words and phrases includes tagging the salient words and phrases. Salient words and phrases are tagged before creating the salient marked-up document. Tagging the salient words and phrases is typically done by a form of markup. Markup can be HTML language, highlighting, bold letters, etc., as long as the markup indicates the salient words and phrases of the original document to create a set of salient words and phrases in which the content-bearing items were found. Alternatively, generating the salient words and phrases can also include erasing text determined to be below the level of salience from the document. This is then used by the text markup means to mark up the text accordingly. The salient text can also be highlighted, or marked up in some other way to represent the higher level of prominence. 
         [0026]    Referring now to  FIG. 4  the reference numeral  400  generally indicates a system for reading the set of salient words and phrases. Once the set of salient words and phrases  196  has been generated, the set of words and phrases can be sent to a reader  198  individually or as part of a salient marked-up document  402 . In the system  400 , the reader  198  includes an audible representations generator  404  capable of producing sounds representing the set of salient words and phrases  196  thru a computer  150  to speakers  406 . Typical embodiments of an audible representations generator include any text-to-speech converter, such as IBM&#39;s text-to-speech engines or Home Page Reader. 
         [0027]    The Home Page Reader is an accessibility product, such as an IBM accessibility product, designed to allow people who are blind or have low vision to “read” the web. It utilizes Web Access Technology (WAT) as its engine to navigate an HTML Document Object Model (DOM), such as the Internet Explorer (IE) DOM. WAT generates data packets containing textual content and information about each web page element, which is represented by one or more DOM nodes. A user interface (UI) component, such as the Home Page Reader browser or a document reader, sends requests for specific types of data packets to WAT based on keyboard, mouse, or document events, such as the loading of a web page or the Tab key being pressed. Upon receiving a data packet from WAT, the UI component extracts the text content and information from the packet to generate both text and speech views of the web page. The text view can distinguish different types of elements, such as key words or phrases, using visual text characteristics such as font size, type, color, and style. The speech view uses different speech characteristics, such as speech rate, different voices, sounds files, and leading or trailing text, to differentiate different types or elements on the web page. To generate speech output, the Home Page Reader incorporates an ability that is capable of converting text to audible sounds. 
         [0028]    The Home Page Reader can read the text aloud. The way the text is read will vary based on the markups. Images can be suppressed, or the ALT tags on the images could be read aloud. For example, words underlined by the document content analyzer could be read louder, or HTML links could be read in a different voice. Home Page Reader reads the full range of the Web page data in a logical, clear and understandable manner. It reads tables, forms, and frames and allows users to search for specific text on a Web page as well as on the entire Web. In addition, Home Page Reader reads HTML 4.0 information provided by Web page authors, giving information such as table summaries and captions. Home Page Reader reads the full range of the Web page data in a logical, clear and understandable manner. 
         [0029]    Not limited to the Home Page Reader, any compatible text-to-speech converter can also be used in the method of analyzing document content for display with reduced cognitive load. To be compatible, a text-to-speech converter must be able to recognize the differences in the text of the marked up document. The method described herein can be embedded in a software product, in which case the software product can mark up the text, and then send it over the internet to a client running a program capable of the Home Page Reader function. 
         [0030]    It is understood that the present invention can take many forms and embodiments. Accordingly, several variations can be made in the foregoing without departing from the spirit or the scope of the invention. The capabilities outlined herein allow for the possibility of a variety of programming models. This disclosure should not be read as preferring any particular programming model, but is instead directed to the underlying mechanisms on which these programming models can be built. 
         [0031]    Having thus described the present invention by reference to certain of its preferred embodiments, it is noted that the embodiments disclosed are illustrative rather than limiting in nature and that a wide range of variations, modifications, changes, and substitutions are contemplated in the foregoing disclosure and, in some instances, some features of the present invention can be employed without a corresponding use of the other features. Many such variations and modifications can be considered desirable by those skilled in the art based upon a review of the foregoing description of preferred embodiments. Accordingly, it is appropriate that the appended claims be construed broadly and in a manner consistent with the scope of the invention.