A Benchmark Suite for Template Detection and Content Extraction

Template detection and content extraction are two of the main areas of information retrieval applied to the Web. They perform different analyses over the structure and content of webpages to extract some part of the document. However, their objective is different. While template detection identifies the template of a webpage (usually comparing with other webpages of the same website), content extraction identifies the main content of the webpage discarding the other part. Therefore, they are somehow complementary, because the main content is not part of the template. It has been measured that templates represent between 40% and 50% of data on the Web. Therefore, identifying templates is essential for indexing tasks because templates usually contain irrelevant information such as advertisements, menus and banners. Processing and storing this information is likely to lead to a waste of resources (storage space, bandwidth, etc.). Similarly, identifying the main content is essential for many information retrieval tasks. In this paper, we present a benchmark suite to test different approaches for template detection and content extraction. The suite is public, and it contains real heterogeneous webpages that have been labelled so that different techniques can be suitable (and automatically) compared.Comment: 13 pages, 3 table

Modeling Human Visual Search Performance on Realistic Webpages Using Analytical and Deep Learning Methods

Modeling visual search not only offers an opportunity to predict the usability of an interface before actually testing it on real users, but also advances scientific understanding about human behavior. In this work, we first conduct a set of analyses on a large-scale dataset of visual search tasks on realistic webpages. We then present a deep neural network that learns to predict the scannability of webpage content, i.e., how easy it is for a user to find a specific target. Our model leverages both heuristic-based features such as target size and unstructured features such as raw image pixels. This approach allows us to model complex interactions that might be involved in a realistic visual search task, which can not be easily achieved by traditional analytical models. We analyze the model behavior to offer our insights into how the salience map learned by the model aligns with human intuition and how the learned semantic representation of each target type relates to its visual search performance.Comment: the 2020 CHI Conference on Human Factors in Computing System

Site-Level Web Template Extraction Based on DOM Analysis

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-41579-6_4One of the main development resources for website engineers are Web templates. Templates allow them to increase productivity by plugin content into already formatted and prepared pagelets. For the final user templates are also useful, 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. In this work we propose a novel method for automatic web template extraction that is based on similarity analysis between the DOM trees of a collection of webpages that are detected using an hyperlink analysis. Our implementation and experiments demonstrate the usefulness of the technique.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Econom´ıa y Competitividad (Secretar´ıa de Estado de Investigaci´on, Desarrollo e Innovaci´on) under grant TIN2013-44742-C4-1-R and by the Generalitat Valenciana under grant PROMETEOII/2015/013. David Insa was partially supported by the Spanish Ministerio de Eduaci´on under FPU grant AP2010-4415.Alarte-Aleixandre, J.; Insa Cabrera, D.; Silva, J.; Tamarit Muñoz, S. (2016). Site-Level Web Template Extraction Based on DOM Analysis. Lecture Notes in Computer Science. 9609:36-49. https://doi.org/10.1007/978-3-319-41579-6S3649960

Information Retrieval Based on DOM Trees

[ES] Desde hace varios años, la cantidad de información disponible en la web crece de manera exponencial. Cada día se genera una gran cantidad de información que prácticamente de inmediato está disponible en la web. Los buscadores e indexadores recorren diariamente la web para encontrar toda esa información que se ha ido añadiendo y así, ponerla a disposición del usuario devolviéndola en los resultados de las búsquedas. Sin embargo, la cantidad de información es tan grande que debe ser preprocesada con anterioridad. Dado que el usuario que realiza una búsqueda de información solamente está interesado en la información relevante, no tiene sentido que los buscadores e indexadores procesen el resto de elementos de las páginas web. El procesado de elementos irrelevantes de páginas web supone un gasto de recursos innecesario, como por ejemplo espacio de almacenamiento, tiempo de procesamiento, uso de ancho de banda, etc. Se estima que entre el 40% y el 50% del contenido de las páginas web son elementos irrelevantes. Por eso, en los últimos 20 años se han desarrollado técnicas para la detección de elementos tanto relevantes como irrelevantes de páginas web. Este objetivo se puede abordar de diversas maneras, por lo que existen técnicas diametralmente distintas para afrontar el problema. Esta tesis se centra en el desarrollo de técnicas basadas en árboles DOM para la detección de diversas partes de las páginas web, como son el contenido principal, la plantilla, y el menú. La mayoría de técnicas existentes se centran en la detección de texto dentro del contenido principal de las páginas web, ya sea eliminando la plantilla de dichas páginas o detectando directamente el contenido principal. Las técnicas que proponemos no sólo son capaces de realizar la extracción de texto, sino que, bien por eliminación de plantilla o bien por detección del contenido principal, son capaces de aislar cualquier elemento relevante de las páginas web, como por ejemplo imágenes, animaciones, videos, etc. Dichas técnicas no sólo son útiles para buscadores y rastreadores, sino que también pueden ser útiles directamente para el usuario que navega por la web. Por ejemplo, en el caso de usuarios con diversidad funcional (como sería una ceguera) puede ser interesante la eliminación de elementos irrelevantes para facilitar la lectura (o escucha) de las páginas web. Para hacer las técnicas accesibles a todo el mundo, las hemos implementado como extensiones del navegador, y son compatibles con navegadores basados en Mozilla o en Chromium. Además, estas herramientas están públicamente disponibles para que cualquier persona interesada pueda acceder a ellas y continuar con la investigación si así lo deseara.[CA] Des de fa diversos anys, la quantitat d'informació disponible en la web creix de manera exponencial. Cada dia es genera una gran quantitat d'informació que immediatament es posa disponible en la web. Els cercadors i indexadors recorren diàriament la web per a trobar tota aqueixa informació que s'ha anat afegint i així, posar-la a la disposició de l'usuari retornant-la en els resultats de les cerques. No obstant això, la quantitat d'informació és tan gran que aquesta ha de ser preprocessada. Atés que l'usuari que realitza una cerca d'informació solament es troba interessat en la informació rellevant, no té sentit que els cercadors i indexadors processen la resta d'elements de les pàgines web. El processament d'elements irrellevants de pàgines web suposa una despesa de recursos innecessària, com per exemple espai d'emmagatzematge, temps de processament, ús d'amplada de banda, etc. S'estima que entre el 40% i el 50% del contingut de les pàgines web són elements irrellevants. Precisament per això, en els últims 20 anys s'han desenvolupat tècniques per a la detecció d'elements tant rellevants com irrellevants de pàgines web. Aquest objectiu es pot afrontar de diverses maneres, per la qual cosa existeixen tècniques diametralment diferents per a afrontar el problema. Aquesta tesi se centra en el desenvolupament de tècniques basades en arbres DOM per a la detecció de diverses parts de les pàgines web, com són el contingut principal, la plantilla, i el menú. La majoria de tècniques existents se centren en la detecció de text dins del contingut principal de les pàgines web, ja siga eliminant la plantilla d'aquestes pàgines o detectant directament el contingut principal. Les tècniques que hi proposem no sols són capaces de realitzar l'extracció de text, sinó que, bé per eliminació de plantilla o bé per detecció del contingut principal, són capaços d'aïllar qualsevol element rellevant de les pàgines web, com per exemple imatges, animacions, vídeos, etc. Aquestes tècniques no sols són útils per a cercadors i rastrejadors, sinó també poden ser útils directament per a l'usuari que navega per la web. Per exemple, en el cas d'usuaris amb diversitat funcional (com ara una ceguera) pot ser interessant l'eliminació d'elements irrellevants per a facilitar-ne la lectura (o l'escolta) de les pàgines web. Per a fer les tècniques accessibles a tothom, les hem implementades com a extensions del navegador, i són compatibles amb navegadors basats en Mozilla i en Chromium. A més, aquestes eines estan públicament disponibles perquè qualsevol persona interessada puga accedir a elles i continuar amb la investigació si així ho desitjara.[EN] For several years, the amount of information available on the Web has been growing exponentially. Every day, a huge amount of data is generated and it is made immediately available on the Web. Indexers and crawlers browse the Web daily to find the new information that has been added, and they make it available to answer the users' search queries. However, the amount of information is so huge that it must be preprocessed. Given that users are only interested in the relevant information, it is not necessary for indexers and crawlers to process other boilerplate, redundant or useless elements of the web pages. Processing such irrelevant elements lead to an unnecessary waste of resources, such as storage space, runtime, bandwidth, etc. Different studies have shown that between 40% and 50% of the data on the Web are noisy elements. For this reason, several techniques focused on the detection of both, relevant and irrelevant data, have been developed over the last 20 years. The problems of identifying the relevant content of a web page, its template, its menu, etc. can be faced in various ways, and for this reason, there exist completely different techniques to address those problems. This thesis is focused on the development of information retrieval techniques based on DOM trees. Its goal is to detect different parts of a web page, such as the main content, the template, and the main menu. Most of the existing techniques are focused on the detection of text inside the main content of the web pages, mainly by removing the template of the web page or by inferring the main content. The techniques proposed in this thesis do not only extract text by eliminating the template or inferring the main content, but also extract any other relevant information from web pages such as images, animations, videos, etc. Our techniques are not only useful for indexers and crawlers but also for the user browsing the Web. For instance, in the case of users with functional diversity problems (such as blindness), removing noisy elements can facilitate them to read (or listen to) the web pages. To make the techniques broadly accessible to everybody, we have implemented them as browser extensions, which are compatible with Mozilla-based and Chromium-based browsers. In addition, these tools are publicly available, so any interested person can access them and continue with the research if they wish to do so.Alarte Aleixandre, J. (2023). Information Retrieval Based on DOM Trees [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19667

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

TeMex: The Web Template Extractor

"© ACM} 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM, In Proceedings of the 24th International Conference on World Wide Web (pp. 155-158), http://dx.doi.org/10.1145/2740908.2742835This paper presents and describes TeMex, a site-level web template extractor. TeMex is fully automatic, and it can work with online webpages without any preprocessing stage (no information about the template or the associated webpages is needed) and, more importantly, it does not need a prede- fined set of webpages to perform the analysis. TeMex only needs a URL. Contrarily to previous approaches, it includes a mechanism to identify webpage candidates that share the same template. This mechanism increases both recall and precision, and it also reduces the amount of webpages loaded and processed. We describe the tool and its internal architecture, and we present the results of its empirical evaluation.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Economía y Competitividad (Secretaría de Estado de Investigación, Desarrollo e Innovación) under Grant TIN2013-44742-C4-1-R and by the Generalitat Valenciana under Grant PROMETEOII/2015/013. David Insa was partially supported by the Spanish Ministerio de Educación under FPU Grant AP2010-4415. Salvador Tamarit was partially supported by research project POLCA, Programming Large Scale Heterogeneous Infrastructures (610686), funded by the European Union, STREP FP7.Alarte, J.; Insa Cabrera, D.; Silva Galiana, JF.; Tamarit Muñoz, S. (2015). TeMex: The Web Template Extractor. ACM. https://doi.org/10.1145/2740908.2742835SOverlay extension. Available from URL: https://developer.mozilla.org/en-US/Add-ons/Overlay_Extensions, 2005.J. Alarte, D. Insa, J. Silva, and S. Tamarit. Automatic Detection of Webpages that Share the Same Web Template. In M. H. ter Beek and A. Ravara, editors, Proceedings of the 10th International Workshop on Automated Specification and Verification of Web Systems (WWV 14), volume 163 of Electronic Proceedings in Theoretical Computer Science, pages 2--15. Open Publishing Association, July 2014.J. Alarte, D. Insa, J. Silva, and S. Tamarit. A Benchmark Suite for Template Detection and Content Extraction. CoRR, abs/1409.6182, 2014.Z. Bar-Yossef and S. Rajagopalan. Template detection via data mining and its applications. In Proceedings of the 11th International Conference on World Wide Web (WWW'02), pages 580--591, New York, NY, USA, 2002. ACM.M. Baroni, F. Chantree, A. Kilgarriff, and S. Sharoff. Cleaneval: a Competition for Cleaning Web Pages. In Proceedings of the International Conference on Language Resources and Evaluation (LREC'08), pages 638--643. European Language Resources Association, may 2008.D. Gibson, K. Punera, and A. Tomkins. The volume and evolution of web page templates. In A. Ellis and T. Hagino, editors, Proceedings of the 14th International Conference on World Wide Web (WWW'05), pages 830--839. ACM, may 2005.T. Gottron. Evaluating content extraction on HTML documents. In V. Grout, D. Oram, and R. Picking, editors, Proceedings of the 2nd International Conference on Internet Technologies and Applications (ITA'07), pages 123--132. National Assembly for Wales, sep 2007.D. d. C. Reis, P. B. Golgher, A. S. Silva, and A. H. F. Laender. Automatic web news extraction using tree edit distance. In Proceedings of the 13th International Conference on World Wide Web (WWW'04), pages 502--511, New York, NY, USA, 2004. ACM.K. Vieira, A. L. da Costa Carvalho, K. Berlt, E. S. de Moura, A. S. da Silva, and J. Freire. On finding templates on web collections. World Wide Web, 12(2):171--211, 2009.K. Vieira, A. S. da Silva, N. Pinto, E. S. de Moura, J. a. M. B. Cavalcanti, and J. Freire. A fast and robust method for web page template detection and removal. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM'06), pages 258--267, New York, NY, USA, 2006. ACM.T. Weninger, W. Henry Hsu, and J. Han. CETR: Content Extraction via Tag Ratios. In M. Rappa, P. Jones, J. Freire, and S. Chakrabarti, editors, Proceedings of the 19th International Conference on World Wide Web (WWW'10), pages 971--980. ACM, apr 2010.L. Yi, B. Liu, and X. Li. Eliminating noisy information in web pages for data mining. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD'03), pages 296--305, New York, NY, USA, 2003. ACM

Democratizing Web Automation: Programming for Social Scientists and Other Domain Experts

We have promised social scientists a data revolution, but it has not arrived. What stands between practitioners and the data-driven insights they want? Acquiring the data. In particular, acquiring the social media, online forum, and other web data that was supposed to help them produce big, rich, ecologically valid datasets. Web automation programming is resistant to high-level abstractions, so end-user programmers end up stymied by the need to reverse engineer website internals—DOM, JavaScript, AJAX. Programming by Demonstration (PBD) offered one promising avenue towards democratizing web automation. Unfortunately, as the web matured, the programs became too complex for PBD tools to synthesize, and web PBD progress stalled.This dissertation describes how I reformulated traditional web PBD around the insight that demonstrations are not always the easiest way for non-programmers to communicate their intent. By shifting from a purely Programming-By-Demonstration view to a Programming-By-X view that accepts a variety of user-friendly inputs, we can dramatically broaden the class of programs that come in reach for end-user programmers. Our Helena ecosystem combines (i) usable PBD-based program drafting tools, (ii) learnable programming languages, and (iii) novel programming environment interactions. The end result: non-coders write Helena programs in 10 minutes that can handle the complexity of modern webpages, while coders attempt the same task and time out in an hour. I conclude with a discussion of the abstraction-resistant domains that will fall next and how hybrid PL-HCI breakthroughs will vastly expand access to programming