A news article generally contains a high-level overview of the facts early on, followed by paragraphs of more detailed information. This structure allows copy editors to truncate the latter paragraphs of an article in order to satisfy space limitations without losing critical information. Existing approaches to this problem of automatic multi-article layout focus exclusively on maximizing content and aesthetics. However, no algorithm can determine how "good" a truncation point is based on the semantic content, or article readability. Yet, disregarding the semantic information within the article can lead to either overly aggressive cutting, thereby eliminating key content and potentially confusing the reader; conversely, it may set too generous of a truncation point, thus leaving in superfluous content and making automatic layout more difficult. This is one of the remaining challenges on the path from manual layouts to fully automated processes with high quality output. In this work, we present a new semantic-focused approach to rate the quality of a truncation point. We built models based on results from an extensive user study on over 700 news articles. Further results show that existing techniques over-cut content. We demonstrate the layout impact through a second evaluation that implements our models in the first layout approach that integrates both layout and semantic quality. The primary contribution of this work is the demonstration that semantic-based modeling is critical for high-quality automated document synthesis within a real-world context.
{"title":"Truncation: all the news that fits we'll print","authors":"J. Hailpern, N. Venkata, Marina Danilevsky","doi":"10.1145/2644866.2644869","DOIUrl":"https://doi.org/10.1145/2644866.2644869","url":null,"abstract":"A news article generally contains a high-level overview of the facts early on, followed by paragraphs of more detailed information. This structure allows copy editors to truncate the latter paragraphs of an article in order to satisfy space limitations without losing critical information. Existing approaches to this problem of automatic multi-article layout focus exclusively on maximizing content and aesthetics. However, no algorithm can determine how \"good\" a truncation point is based on the semantic content, or article readability. Yet, disregarding the semantic information within the article can lead to either overly aggressive cutting, thereby eliminating key content and potentially confusing the reader; conversely, it may set too generous of a truncation point, thus leaving in superfluous content and making automatic layout more difficult. This is one of the remaining challenges on the path from manual layouts to fully automated processes with high quality output. In this work, we present a new semantic-focused approach to rate the quality of a truncation point. We built models based on results from an extensive user study on over 700 news articles. Further results show that existing techniques over-cut content. We demonstrate the layout impact through a second evaluation that implements our models in the first layout approach that integrates both layout and semantic quality. The primary contribution of this work is the demonstration that semantic-based modeling is critical for high-quality automated document synthesis within a real-world context.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"90 1","pages":"165-174"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72894111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Creating web applications for the multiscreen environment is still a challenge. One approach is to transform existing single-screen applications but this has not been done yet automatically or generically. This paper proposes a refactoring system. It consists of a generic and extensible mapping phase that automatically analyzes the application content based on a semantic or a visual criterion determined by the author or the user, and prepares it for the splitting process. The system then splits the application and as a result delivers two instrumented applications ready for distribution across devices. During runtime, the system uses a mirroring phase to maintain the functionality of the distributed application and to support a dynamic splitting process. Developed as a Chrome extension, our approach is validated on several web applications, including a YouTube page and a video application from Mozilla.
{"title":"The virtual splitter: refactoring web applications for themultiscreen environment","authors":"Mira Sarkis, C. Concolato, Jean-Claude Dufourd","doi":"10.1145/2644866.2644893","DOIUrl":"https://doi.org/10.1145/2644866.2644893","url":null,"abstract":"Creating web applications for the multiscreen environment is still a challenge. One approach is to transform existing single-screen applications but this has not been done yet automatically or generically. This paper proposes a refactoring system. It consists of a generic and extensible mapping phase that automatically analyzes the application content based on a semantic or a visual criterion determined by the author or the user, and prepares it for the splitting process. The system then splits the application and as a result delivers two instrumented applications ready for distribution across devices. During runtime, the system uses a mirroring phase to maintain the functionality of the distributed application and to support a dynamic splitting process. Developed as a Chrome extension, our approach is validated on several web applications, including a YouTube page and a video application from Mozilla.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"1 1","pages":"139-142"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89425276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many companies still operate critical business processes using paper-based forms, including customer surveys, inspections, contracts and invoices. Converting those handwritten forms to symbolic data is expensive and complicated. This paper presents an overview of the Image-Based Document Management (IBDM) system for analyzing handwritten forms without requiring conversion to symbolic data. Strokes captured in a questionnaire on a tablet are separated into fields that are then displayed in a spreadsheet. Rows represent documents while columns represent corresponding fields across all documents. IBDM allows a process owner to capture and analyze large collections of documents with minimal IT support. IBDM supports the creation of filters and queries on the data. IBDM also allows the user to request symbolic conversion of individual columns of data and permits the user to create custom views by reordering and sorting the columns. In other words, IBDM provides a "writing on paper" experience for the data collector and a web-based database experience for the analyst.
{"title":"Image-based document management: aggregating collections of handwritten forms","authors":"J. Barrus, E. L. Schwartz","doi":"10.1145/2644866.2644891","DOIUrl":"https://doi.org/10.1145/2644866.2644891","url":null,"abstract":"Many companies still operate critical business processes using paper-based forms, including customer surveys, inspections, contracts and invoices. Converting those handwritten forms to symbolic data is expensive and complicated. This paper presents an overview of the Image-Based Document Management (IBDM) system for analyzing handwritten forms without requiring conversion to symbolic data. Strokes captured in a questionnaire on a tablet are separated into fields that are then displayed in a spreadsheet. Rows represent documents while columns represent corresponding fields across all documents. IBDM allows a process owner to capture and analyze large collections of documents with minimal IT support. IBDM supports the creation of filters and queries on the data. IBDM also allows the user to request symbolic conversion of individual columns of data and permits the user to create custom views by reordering and sorting the columns. In other words, IBDM provides a \"writing on paper\" experience for the data collector and a web-based database experience for the analyst.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"78 1","pages":"117-120"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83524681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Detecting and understanding changes between document revisions is an important task. The acquired knowledge can be used to classify the nature of a new document revision or to support a human editor in the review process. While purely textual change detection algorithms offer fine-grained results, they do not understand the syntactic meaning of a change. By representing structured text documents as XML documents we can apply tree-to-tree correction algorithms to identify the syntactic nature of a change. Many algorithms for change detection in XML documents have been propsed but most of them focus on the intricacies of generic XML data and emphasize speed over the quality of the result. Structured text requires a change detection algorithm to pay close attention to the content in text nodes, however, recent algorithms treat text nodes as black boxes. We present an algorithm that combines the advantages of the purely textual approach with the advantages of tree-to-tree change detection by redistributing text from non-overlapping common substrings to the nodes of the trees. This allows us to not only spot changes in the structure but also in the text itself, thus achieving higher quality and a fine-grained result in linear time on average. The algorithm is evaluated by applying it to the corpus of structured text documents that can be found in the English Wikipedia.
{"title":"Fine-grained change detection in structured text documents","authors":"Hannes Dohrn, D. Riehle","doi":"10.1145/2644866.2644880","DOIUrl":"https://doi.org/10.1145/2644866.2644880","url":null,"abstract":"Detecting and understanding changes between document revisions is an important task. The acquired knowledge can be used to classify the nature of a new document revision or to support a human editor in the review process. While purely textual change detection algorithms offer fine-grained results, they do not understand the syntactic meaning of a change. By representing structured text documents as XML documents we can apply tree-to-tree correction algorithms to identify the syntactic nature of a change.\u0000 Many algorithms for change detection in XML documents have been propsed but most of them focus on the intricacies of generic XML data and emphasize speed over the quality of the result. Structured text requires a change detection algorithm to pay close attention to the content in text nodes, however, recent algorithms treat text nodes as black boxes.\u0000 We present an algorithm that combines the advantages of the purely textual approach with the advantages of tree-to-tree change detection by redistributing text from non-overlapping common substrings to the nodes of the trees. This allows us to not only spot changes in the structure but also in the text itself, thus achieving higher quality and a fine-grained result in linear time on average. The algorithm is evaluated by applying it to the corpus of structured text documents that can be found in the English Wikipedia.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"149 1","pages":"87-96"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79440986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automatic text segmentation, which is the task of breaking a text into topically-consistent segments, is a fundamental problem in Natural Language Processing, Document Classification and Information Retrieval. Text segmentation can significantly improve the performance of various text mining algorithms, by splitting heterogeneous documents into homogeneous fragments and thus facilitating subsequent processing. Applications range from screening of radio communication transcripts to document summarization, from automatic document classification to information visualization, from automatic filtering to security policy enforcement - all rely on, or can largely benefit from, automatic document segmentation. In this article, a novel approach for automatic text and data stream segmentation is presented and studied. The proposed automatic segmentation algorithm takes advantage of feature extraction and unusual behaviour detection algorithms developed in [4, 5]. It is entirely unsupervised and flexible to allow segmentation at different scales, such as short paragraphs and large sections. We also briefly review the most popular and important algorithms for automatic text segmentation and present detailed comparisons of our approach with several of those state-of-the-art algorithms.
{"title":"On automatic text segmentation","authors":"Boris Dadachev, A. Balinsky, H. Balinsky","doi":"10.1145/2644866.2644874","DOIUrl":"https://doi.org/10.1145/2644866.2644874","url":null,"abstract":"Automatic text segmentation, which is the task of breaking a text into topically-consistent segments, is a fundamental problem in Natural Language Processing, Document Classification and Information Retrieval. Text segmentation can significantly improve the performance of various text mining algorithms, by splitting heterogeneous documents into homogeneous fragments and thus facilitating subsequent processing. Applications range from screening of radio communication transcripts to document summarization, from automatic document classification to information visualization, from automatic filtering to security policy enforcement - all rely on, or can largely benefit from, automatic document segmentation. In this article, a novel approach for automatic text and data stream segmentation is presented and studied. The proposed automatic segmentation algorithm takes advantage of feature extraction and unusual behaviour detection algorithms developed in [4, 5]. It is entirely unsupervised and flexible to allow segmentation at different scales, such as short paragraphs and large sections. We also briefly review the most popular and important algorithms for automatic text segmentation and present detailed comparisons of our approach with several of those state-of-the-art algorithms.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"9 1","pages":"73-80"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89114138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The potential for semi-supervised techniques to produce personalized clusters has not been explored. This is due to the fact that semi-supervised clustering algorithms used to be evaluated using oracles based on underlying class labels. Although using oracles allows clustering algorithms to be evaluated quickly and without labor intensive labeling, it has the key disadvantage that oracles always give the same answer for an assignment of a document or a feature. However, different human users might give different assignments of the same document and/or feature because of different but equally valid points of view. In this paper, we conduct a user study in which we ask participants (users) to group the same document collection into clusters according to their own understanding, which are then used to evaluate semi-supervised clustering algorithms for user personalization. Through our user study, we observe that different users have their own personalized organizations of the same collection and a user's organization changes over time. Therefore, we propose that document clustering algorithms should be able to incorporate user input and produce personalized clusters based on the user input. We also confirm that semi-supervised algorithms with noisy user input can still produce better organizations matching user's expectation (personalization) than traditional unsupervised ones. Finally, we demonstrate that labeling keywords for clusters at the same time as labeling documents can improve clustering performance further compared to labeling only documents with respect to user personalization.
{"title":"Personalized document clustering with dual supervision","authors":"Yeming Hu, E. Milios, J. Blustein, Shali Liu","doi":"10.1145/2361354.2361393","DOIUrl":"https://doi.org/10.1145/2361354.2361393","url":null,"abstract":"The potential for semi-supervised techniques to produce personalized clusters has not been explored. This is due to the fact that semi-supervised clustering algorithms used to be evaluated using oracles based on underlying class labels. Although using oracles allows clustering algorithms to be evaluated quickly and without labor intensive labeling, it has the key disadvantage that oracles always give the same answer for an assignment of a document or a feature. However, different human users might give different assignments of the same document and/or feature because of different but equally valid points of view. In this paper, we conduct a user study in which we ask participants (users) to group the same document collection into clusters according to their own understanding, which are then used to evaluate semi-supervised clustering algorithms for user personalization. Through our user study, we observe that different users have their own personalized organizations of the same collection and a user's organization changes over time. Therefore, we propose that document clustering algorithms should be able to incorporate user input and produce personalized clusters based on the user input. We also confirm that semi-supervised algorithms with noisy user input can still produce better organizations matching user's expectation (personalization) than traditional unsupervised ones. Finally, we demonstrate that labeling keywords for clusters at the same time as labeling documents can improve clustering performance further compared to labeling only documents with respect to user personalization.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"59 Pt A 1","pages":"161-170"},"PeriodicalIF":0.0,"publicationDate":"2012-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86924607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jack Jansen, Pablo César, R. Guimarães, D. Bulterman
Using high-quality video cameras on mobile devices, it is relatively easy to capture a significant volume of video content for community events such as local concerts or sporting events. A more difficult problem is selecting and sequencing individual media fragments that meet the personal interests of a viewer of such content. In this paper, we consider an infrastructure that supports the just-in-time delivery of personalized content. Based on user profiles and interests, tailored video mash-ups can be created at view-time and then further tailored to user interests via simple end-user interaction. Unlike other mash-up research, our system focuses on client-side compilation based on personal (rather than aggregate) interests. This paper concentrates on a discussion of language and infrastructure issues required to support just-in-time video composition and delivery. Using a high school concert as an example, we provide a set of requirements for dynamic content delivery. We then provide an architecture and infrastructure that meets these requirements. We conclude with a technical and user analysis of the just-in-time personalized video approach.
{"title":"Just-in-time personalized video presentations","authors":"Jack Jansen, Pablo César, R. Guimarães, D. Bulterman","doi":"10.1145/2361354.2361368","DOIUrl":"https://doi.org/10.1145/2361354.2361368","url":null,"abstract":"Using high-quality video cameras on mobile devices, it is relatively easy to capture a significant volume of video content for community events such as local concerts or sporting events. A more difficult problem is selecting and sequencing individual media fragments that meet the personal interests of a viewer of such content. In this paper, we consider an infrastructure that supports the just-in-time delivery of personalized content. Based on user profiles and interests, tailored video mash-ups can be created at view-time and then further tailored to user interests via simple end-user interaction. Unlike other mash-up research, our system focuses on client-side compilation based on personal (rather than aggregate) interests. This paper concentrates on a discussion of language and infrastructure issues required to support just-in-time video composition and delivery. Using a high school concert as an example, we provide a set of requirements for dynamic content delivery. We then provide an architecture and infrastructure that meets these requirements. We conclude with a technical and user analysis of the just-in-time personalized video approach.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"1 1","pages":"59-68"},"PeriodicalIF":0.0,"publicationDate":"2012-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83657243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider the problem of automatically inserting advertisements (ads) into machine composed documents. We explicitly analyze the fundamental tradeoff between expected revenue due to ad insertion and the quality of the corresponding composed documents. We show that the optimal tradeoff a publisher can expect may be expressed as an efficient-frontier in the revenue-quality space. We develop algorithms to compose documents that lie on this optimal tradeoff frontier. These algorithms can automatically choose distributions of ad sizes and ad placement locations to optimize revenue for a given quality or optimize quality for given revenue. Such automation allows a market maker to accept highly personalized content from publishers who have no design or ad inventory management capability and distribute formatted documents to end users with aesthetic ad placement. The ad density/coverage may be controlled by the publisher or the end user on a per document basis by simply sliding along the tradeoff frontier. Business models where ad sales precede (ad-pull) or follow (ad-push) document composition are analyzed from a document engineering perspective.
{"title":"Ad insertion in automatically composed documents","authors":"Niranjan Damera-Venkata, José Bento","doi":"10.1145/2361354.2361358","DOIUrl":"https://doi.org/10.1145/2361354.2361358","url":null,"abstract":"We consider the problem of automatically inserting advertisements (ads) into machine composed documents. We explicitly analyze the fundamental tradeoff between expected revenue due to ad insertion and the quality of the corresponding composed documents. We show that the optimal tradeoff a publisher can expect may be expressed as an efficient-frontier in the revenue-quality space. We develop algorithms to compose documents that lie on this optimal tradeoff frontier. These algorithms can automatically choose distributions of ad sizes and ad placement locations to optimize revenue for a given quality or optimize quality for given revenue. Such automation allows a market maker to accept highly personalized content from publishers who have no design or ad inventory management capability and distribute formatted documents to end users with aesthetic ad placement. The ad density/coverage may be controlled by the publisher or the end user on a per document basis by simply sliding along the tradeoff frontier. Business models where ad sales precede (ad-pull) or follow (ad-push) document composition are analyzed from a document engineering perspective.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"82 1","pages":"3-12"},"PeriodicalIF":0.0,"publicationDate":"2012-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87378701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yogalakshmi Jayabal, Chandrashekar Ramanathan, M. Sheth
ABSTRACT The task of extracting document structures from a digital e-book is difficult and is an active area of research. On the other hand, many e-books already have a table of contents (TOC) at the beginning of the document. This may lead us to believe that adding bookmarks into digital document (e-book) based on the existing TOC would be trivial. In this paper, we highlight the challenges involved in this task of automatically adding bookmarks to an existing e-book based on the TOC that exists within the document. If we are able to reliably identify the specific locations of each TOC entry within the document, the algorithms can be easily extended to identify document structures within e-books that have TOC. We describe a tool we have built called Booky that tries to add automatic PDF bookmarks to existing PDF based e-books as they have TOC as part of the document content. The tool addresses most of the challenges that have been identified while still leaving a few tricky scenarios still open.
{"title":"Challenges in generating bookmarks from TOC entries in e-books","authors":"Yogalakshmi Jayabal, Chandrashekar Ramanathan, M. Sheth","doi":"10.1145/2361354.2361363","DOIUrl":"https://doi.org/10.1145/2361354.2361363","url":null,"abstract":"ABSTRACT The task of extracting document structures from a digital e-book is difficult and is an active area of research. On the other hand, many e-books already have a table of contents (TOC) at the beginning of the document. This may lead us to believe that adding bookmarks into digital document (e-book) based on the existing TOC would be trivial. In this paper, we highlight the challenges involved in this task of automatically adding bookmarks to an existing e-book based on the TOC that exists within the document. If we are able to reliably identify the specific locations of each TOC entry within the document, the algorithms can be easily extended to identify document structures within e-books that have TOC. We describe a tool we have built called Booky that tries to add automatic PDF bookmarks to existing PDF based e-books as they have TOC as part of the document content. The tool addresses most of the challenges that have been identified while still leaving a few tricky scenarios still open.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"62 1","pages":"37-40"},"PeriodicalIF":0.0,"publicationDate":"2012-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86104494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
César F. Acebal, B. Bos, M. Rodríguez, J. M. C. Lovelle
Traditionally, web standards in general and Cascading Style Sheets (CSS) in particular take a long time from when they are defined by the W3C until they are implemented by browser vendors. This has been a limitation not only for authors, who had to wait even years before they were able to use certain CSS properties in their web pages, but also for the creators of the specification itself, who were not able to test their proposals in practice. In this paper we present ALMcss, a JavaScript prototype that implements the CSS Template Layout Module, a proposal for an addition to CSS to make it a more capable layout language. It has been developed inside the W3C CSS Working Group by two of the authors of this paper. We present the rationale of the module and an introduction to its syntax, before discussing the design of our prototype. ALMcss has served us as a proof of concept that the Template Layout Module is not only feasible, but it can be in fact implemented in current web browsers using just JavaScript and the Document Object Model (DOM). In addition, ALMcss allows web designers to start to use today the new layout capabilities of CSS that the module provides, even before it becomes an official W3C specification.
{"title":"ALMcss: a javascript implementation of the CSS template layout module","authors":"César F. Acebal, B. Bos, M. Rodríguez, J. M. C. Lovelle","doi":"10.1145/2361354.2361360","DOIUrl":"https://doi.org/10.1145/2361354.2361360","url":null,"abstract":"Traditionally, web standards in general and Cascading Style Sheets (CSS) in particular take a long time from when they are defined by the W3C until they are implemented by browser vendors. This has been a limitation not only for authors, who had to wait even years before they were able to use certain CSS properties in their web pages, but also for the creators of the specification itself, who were not able to test their proposals in practice.\u0000 In this paper we present ALMcss, a JavaScript prototype that implements the CSS Template Layout Module, a proposal for an addition to CSS to make it a more capable layout language. It has been developed inside the W3C CSS Working Group by two of the authors of this paper. We present the rationale of the module and an introduction to its syntax, before discussing the design of our prototype.\u0000 ALMcss has served us as a proof of concept that the Template Layout Module is not only feasible, but it can be in fact implemented in current web browsers using just JavaScript and the Document Object Model (DOM). In addition, ALMcss allows web designers to start to use today the new layout capabilities of CSS that the module provides, even before it becomes an official W3C specification.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"10 1","pages":"23-32"},"PeriodicalIF":0.0,"publicationDate":"2012-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88182088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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