## 3 Related Work  
While the readability indices we recalled in Section 2 were originally developed for text documents, they are nowadays applied to web documents, too. Typical applications in this context are to assess how comprehensible legal statements are [1] or to let users select documents most appropriate to their educational level. The latter application can be subdivided further into ranking web search results according to their readability [2] or to filter documents that do not comply with the reading ability of a given user [3,4]. So far, little work consider the differences in the calculation of readability metrics for web documents. [PERSON] and [PERSON] [15] considered in particular the distinction between web documents that contain little text and for which it consequently is not suitable to compute a readability score at all. This distinction is based on a supervised learning approach using very common words as feature set for the documents. Few systems address the problem of identifying the actual main content in a web document when computing its readability. [PERSON] _et al_. [2] present a domain and language dependent approach to detect the particular structure of how-to manuals. The Read-X system [4,16] instead, uses a heuristic content extraction tool to improve the estimation of readability. The extraction module in Read-X is based on a screen scraper software library supporting the development of hand crafted extraction filters for template based documents. However, so far no system involves state-of-the-art generic content extraction algorithms. Generic content extraction algorithms are covered in various publications. Typically the algorithms are based on heuristics and follow some assumptions about the shape, structure and form of the main content. A thorough evaluation of different approaches is presented in [17]. In this comparison the Document Slope Curve filter [18] showed good efficiency and effectiveness. Newer methods have advanced the accuracy (e.g., Content Code Blurring [19]), efficiency (e.g., the Density algorithm [20]) or addressed language specific scenarios (e.g., the DANA approach for arabian languages [21]). All modern CE methods are highly efficient, are capable of processing between 10 and 20 MB of web documents data per second on commodity hardware and are, thus, suitable for on-the-fly application for cleaning web documents from noise. State-of-the-art extraction algorithms achieve an extraction performance with an average F1 score between 0.86 and 0.96, depending on the application scenario. A detailed analysis also showed that some methods are more biased towards a higher precision, while others favour a high recall in extracting the main content [17]. In [22] we looked at the influence of noise in web documents--such as navigation menus, related links lists, headers, footers, disclaimers--on the automatically determined readability score. We found that content extraction algorithms like the Document Slope Curve filter [18] or Content Code Blurring [19] led to much better estimates of the readability of the document's actual main content. [PERSON] _et al_. [23] investigate domain specific readability. They explain that domain specific texts have technical or professional terms. These terms cannot be measured by traditional syllable counting like SMOG or FRE. Common words will become technical terms in domain specific texts. They also propose to compute a relative readability score rather an absolute grade level metrics. They present several complex formulae to calculate their concept-based document readability. They include calculations on word level, consider a given knowledge base and document scope, and also calculate general words which are out of a common word list. [PERSON] _et al_. state that traditional readability formulae are oversimplified. The drawback of their approach instead lies in the need for a domain expert to formulate the required knowledge base.