What Web Template Extractor Should I Use? A Benchmarking and Comparison for Five Template Extractors

"© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, {VOL 13, ISS 2, (APR 2019)} http://doi.acm.org/10.1145/3316810"[EN] A Web template is a resource that implements the structure and format of a website, making it ready for plugging content into already formatted and prepared pages. For this reason, templates are one of the main development resources for website engineers, because they increase productivity. Templates are also useful for the final user, because they provide uniformity and a common look and feel for all webpages. However, from the point of view of crawlers and indexers, templates are an important problem, because templates usually contain irrelevant information, such as advertisements, menus, and banners. Processing and storing this information leads to a waste of resources (storage space, bandwidth, etc.). It has been measured that templates represent between 40% and 50% of data on the Web. Therefore, identifying templates is essential for indexing tasks. There exist many techniques and tools for template extraction, but, unfortunately, it is not clear at all which template extractor should a user/system use, because they have never been compared, and because they present different (complementary) features such as precision, recall, and efficiency. In this work, we compare the most advanced template extractors. We implemented and evaluated five of the most advanced template extractors in the literature. To compare all of them, we implemented a workbench, where they have been integrated and evaluated. Thanks to this workbench, we can provide a fair empirical comparison of all methods using the same benchmarks, technology, implementation language, and evaluation criteria.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Ciencia, Innovacion y Universidades/AEI under grant TIN2016-76843-C4-1-R and by the Generalitat Valenciana under grants PROMETEO-II/2015/013 (SmartLogic) and Prometeo/2019/098 (DeepTrust).Alarte, J.; Silva, J.; Tamarit Muñoz, S. (2019). What Web Template Extractor Should I Use? A Benchmarking and Comparison for Five Template Extractors. ACM Transactions on the Web. 13(2):9:1-9:19. https://doi.org/10.1145/3316810S9:19:19132Alarte, J., Insa, D., Silva, J., & Tamarit, S. (2015). TeMex. Proceedings of the 24th International Conference on World Wide Web - WWW ’15 Companion. doi:10.1145/2740908.2742835Julián Alarte David Insa Josep Silva and Salvador Tamarit. 2016. Site-Level Web Template Extraction Based on DOM Analysis. 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Web Content Extraction - a Meta-Analysis of its Past and Thoughts on its Future

In this paper, we present a meta-analysis of several Web content extraction algorithms, and make recommendations for the future of content extraction on the Web. First, we find that nearly all Web content extractors do not consider a very large, and growing, portion of modern Web pages. Second, it is well understood that wrapper induction extractors tend to break as the Web changes; heuristic/feature engineering extractors were thought to be immune to a Web site's evolution, but we find that this is not the case: heuristic content extractor performance also tends to degrade over time due to the evolution of Web site forms and practices. We conclude with recommendations for future work that address these and other findings.Comment: Accepted for publication in SIGKDD Exploration

Page-Level Main Content Extraction from Heterogeneous Webpages

[EN] The main content of a webpage is often surrounded by other boilerplate elements related to the template, such as menus, advertisements, copyright notices, and comments. For crawlers and indexers, isolating the main content from the template and other noisy information is an essential task, because processing and storing noisy information produce a waste of resources such as bandwidth, storage space, and computing time. Besides, the detection and extraction of the main content is useful in different areas, such as data mining, web summarization, and content adaptation to low resolutions. This work introduces a new technique for main content extraction. In contrast to most techniques, this technique not only extracts text, but also other types of content, such as images, and animations. It is a Document Object Model-based page-level technique, thus it only needs to load one single webpage to extract the main content. As a consequence, it is efficient enough as to be used online (in real-time). We have empirically evaluated the technique using a suite of real heterogeneous benchmarks producing very good results compared with other well-known content extraction techniques.This work has been partially supported by the EU (FEDER) and the Spanish MCI/AEI under grants TIN2016-76843-C4-1-R and PID2019-104735RB-C41, by the Generalitat Valenciana under grant Prometeo/2019/098 (DeepTrust), and by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.Alarte, J.; Silva, J. (2021). Page-Level Main Content Extraction from Heterogeneous Webpages. ACM Transactions on Knowledge Discovery from Data. 15(6):1-21. https://doi.org/10.1145/3451168S12115

Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media

Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention. To attract the readers to click on an article and subsequently visit the media site, the outlets often come up with catchy headlines accompanying the article links, which lure the readers to click on the link. Such headlines are known as Clickbaits. While these baits may trick the readers into clicking, in the long run, clickbaits usually don't live up to the expectation of the readers, and leave them disappointed. In this work, we attempt to automatically detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines. The extension also offers each reader an option to block clickbaits she doesn't want to see. Then, using such reader choices, the extension automatically blocks similar clickbaits during her future visits. We run extensive offline and online experiments across multiple media sites and find that the proposed clickbait detection and the personalized blocking approaches perform very well achieving 93% accuracy in detecting and 89% accuracy in blocking clickbaits.Comment: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM

Lifting user generated comments to SIOC

International audienceHTML boilerplate code is acting on webpages as presentation directives for a browser to display data to a human end user. For the machine, our community made tremenduous e orts to provide querying endpoints using consensual schemas, protocols, and principles since the avent of the Linked Data paradigm. These data lifting e orts have been the primary materials for bootstraping the Web of data. Data lifting usually involves an original data structure from which the semantic architect has to produce a mapper to RDF vocabularies. Less e orts are made in order to lift data produced by a Web mining process, due to the di culty to provide an e cient and scalable solution. Nonetheless, the Web of documents is mainly composed of natural language twisted in HTML boilerplate code, and few data schemas can be mapped into RDF. In this paper, we present CommentsLifter, a system that is able to lift SIOC data from user-generated comments in the Web 2.0

A framework for capturing domain knowledge via the web

Domain knowledge can be formalized and represented by ontologies, which play an important role in the realization of the Semantic Web. However, since the acquisition of knowledge from certain domains usually requires deep involvement of qualified domain experts, construction of such ontologies is difficult and costly, even with the availability of dedicated languages and ontology editing tools. Some effect has been made to reduce this involvement by introducing a general paradigm of automatic domain knowledge learning from various sources. To make this paradigm more specific and practical, this paper proposes a framework for capturing domain knowledge through raw domain data available over the Web. This framework consists of three dedicated parts: data collection, pre-processing and mining, where mining part performs core task of the framework. Each part can be designed with specific optimized methods. The preliminary implementation of certain parts has shown it is able to capture the knowledge of electronic product taxonomy via the Web. © 2005. Chao Wang, Jie Lu & Guangquan Zhang

Towards automatic extraction of definitions

Definition extraction can be useful for the creation of glossaries and in question answering systems. It is a tedious task to extract such sentences manually, and thus an automatic system is desirable. In this work we review various attempts at rule-based approaches reported in the literature and discuss their results. We also propose a novel experiment involving the use of genetic programming and genetic algorithms, aimed at assisting the discovery of grammar rules which can be used for the task of definition extraction.peer-reviewe