Automatic selection of print-worthy content for enhanced web page printing experience

Lim, Suk Hwan; Li-wei, Zheng; Jin, Jianming; Hou, Huiman; Fan, Jian; Liu, Jerry

doi:10.1145/1860559.1860592

Cited by 8 publications

(9 citation statements)

References 7 publications

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“…Extracting web content from web pages has received substantial interest by researchers [13,4,6,10,12,18,21,8,17,9,19,16]. Most of existent working has focused on article web pages.…”

Section: Related Workmentioning

confidence: 99%

“…The task of extracting the main content from web pages has attracted many research works [4,10,12,13,18]. However, most of these previous works focus on article web pages, such as news articles, and blog posts.…”

Section: Introductionmentioning

confidence: 99%

“…These previous solutions that were only demonstrated to work for article web pages are not very likely to work for nonarticle web pages. To help users to get more accurate main content, the tool of Smart Print provides for Web page printing [10] provides users an option to manually select the informative areas of a web page in an interactive interface. The more manual work that needs to get involved in the tool, the worse the user experience would be.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Web Content Extraction by Combination of Learning and Grouping

Liu

Fan

2015

Proceedings of the 24th International Conference on World Wide Web

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Web pages consist of not only actual content, but also other elements such as branding banners, navigational elements, advertisements, copyright etc. This noisy content is typically not related to the main subjects of the webpages. Identifying the part of actual content, or clipping web pages, has many applications, such as high quality web printing, e-reading on mobile devices and data mining. Although there are many existing methods attempting to address this task, most of them can either work only on certain types of Web pages, e.g. article pages, or has to develop different models for different websites. We formulate the actual content identifying problem as a DOM tree node selection problem. We develop multiple features by utilizing the DOM tree node properties to train a machine learning model. Then candidate nodes are selected based on the learning model. Based on the observation that the actual content is usually located in a spatially continuous block, we develop a grouping technology to further filter out noisy data and pick missing data for the candidate nodes. We conduct extensive experiments on a real dataset and demonstrate our solution has high quality outputs and outperforms several baseline methods.

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“…Extracting web content from web pages has received substantial interest by researchers [13,4,6,10,12,18,21,8,17,9,19,16]. Most of existent working has focused on article web pages.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Web Content Extraction by Combination of Learning and Grouping

Liu

Fan

2015

Proceedings of the 24th International Conference on World Wide Web

Self Cite

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“…To select relevant content from the web page, we leverage the algorithms that are used in the HP Smart Print browser plug-in [6], the details for which are explained in prior publication [7]. This algorithm relies only on the visual appearance of the web page, making no assumptions about the language encoding or the semantics of the page, and thus is usable across all web pages regardless of type or national origin.…”

Section: Our Solutionmentioning

confidence: 99%

A URL shortener for mobile web consumption

Zhang¹,

Wu²,

Zheng³

et al. 2012

Imaging and Printing in a Web 2.0 World III

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We present hp2.me, a URL shortener service for improving the mobile web consumption experience. Unlike other such services, given a short URL, hp2.me returns an image rendered from the salient regions of a web page. This approach to displaying web content improves mobile web reading experience through reduced latency and improved clarity. It is faster to load a few large image files over a cellular data network than many small files, and limited mobile screen real estate can be better utilized to display relevant content. The hp2.me service is currently in external beta, and we present results to illustrate the advantages of this technique.

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“…However, Web pages encoded by the HTML language are designed for viewing on PC screen and may not be suitable for other purposes, such as Web printing and e-reading on small handheld devices. Thus, extracting informative clips (referring to content areas in Web pages) attracts many research works [12,14,3,10,9]. However, most of these previous works focus on extracting article Web pages, such as news stories, encyclopedia entries, and blog posts.…”

Section: Introductionmentioning

confidence: 99%

Harnessing the wisdom of the crowds for accurate web page clipping

Zhang

Tang

Luo³

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Clipping Web pages, namely extracting the informative clips (areas) from Web pages, has many applications, such as Web printing and e-reading on small handheld devices. Although many existing methods attempt to address this task, most of them can either work only on certain types of Web pages (e.g., news-and bloglike web pages), or perform semi-automatically where extra user efforts are required in adjusting the outputs. The problem of clipping any types of Web pages accurately in a totally automatic way remains pretty much open. To this end in this study we harness the wisdom of the crowds to provide accurate recommendation of informative clips on any given Web pages. Specifically, we leverage the knowledge on how previous users clip similar Web pages, and this knowledge repository can be represented as a transaction database where each transaction contains the clips selected by a user on a certain Web page. Then, we formulate a new pattern mining problem, mining top-1 qualified pattern, on transaction database for this recommendation. Here, the recommendation considers not only the pattern support but also the pattern occupancy (proposed in this work). High support requires that patterns appear frequently in the database, while high occupancy requires that patterns occupy a large portion of the transactions they appear in. Thus, it leads to both precise and complete recommendation. Additionally, we explore the properties on occupancy to further prune the search space for high-efficient pattern mining. Finally, we show the effectiveness of the proposed algorithm on a human-labeled ground truth dataset consisting of 2000 web pages from 100 major Web sites, and demonstrate its efficiency on large synthetic datasets.

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Automatic selection of print-worthy content for enhanced web page printing experience

Cited by 8 publications

References 7 publications

Automatic Web Content Extraction by Combination of Learning and Grouping

Automatic Web Content Extraction by Combination of Learning and Grouping

A URL shortener for mobile web consumption

Harnessing the wisdom of the crowds for accurate web page clipping

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