## 1 Introduction  
Analysing a text for its readability, _i.e._, the ease to read and comprehend the text, has a long tradition in literature. Since the nineteenth century, researchers in linguistics and literature science have been concerned with the question of how to measure the readability of a text. In recent years, the findings of this established field of research have found their application on web documents as well. Here, the notion of the readability of a document can serve several purposes. Readability metrics have been employed to assess the usability of web sites [1], as a static quality metric for ranking web search results [2] or to filter documents which match a user's reading ability [3; 4; 5; 6]. However, the differences between classical print media and the web are often neglected in this transfer of readability metrics to the web. The differences of these media are twofold. On the one hand, text in the web is presented differently than in a printed book or magazine. This is motivated by technical restrictions on one or the other side, _i.e._, a fixed page size _vs._ a scrollable screen viewport or the navigation via a table of contents and a look-up index with page numbers _vs._ the navigation via hyperlink structures and navigation menus. On the other hand, users consume text contents on the web differently compared to classical print media. Also here, the differences arise from usage patterns supported by the technologies, e.g., scanning and selective reading _vs._ linear and complete reading. In particular, user do not read all the text in a web document, but concentrate on the actually relevant parts while ignoring additional contents, such as navigation menus or advertisements. Given these differences in the presentation and consumption of text, the application of readability metrics for the purpose of web documents needs to be reconsidered. In this paper we address this topic under several aspects. First we consider the problem of text noise in web documents. This noise represents additional texts in a document, that are typically not part of the main content and are perceived differently by a user. Obviously this noise should not be considered when determining the readability of a web document. One way to eliminate the noise is to provide hand crafted filters for cleaning the documents of a particular web site, which are all based on a common template. This approach requires to track changes in the templates [7] and to adapt the filters. Further, implementing hand crafted filters does not scale for an arbitrarily large number of web sites. Thus, here we analyse and compare generic methods for removing the text noise in web document, so called content extraction (CE) algorithms. CE algorithms are generally applicable to all web documents and use document structure, text density or layout features to identify the main text content. As all state-of-the-art CE algorithms are based on heuristics and typically are not perfect, we look in a second step at the bias these methods introduce in the computation of readability metrics. As this bias is systematic we finally propose corrective measures to counterbalance the bias. Finally, we investigate the potential of using the web itself to define metrics for document readability. Altogether in this paper we make several contributions. * We bring together the works of readability assessment on web documents with content extraction techniques. * We qualitatively evaluate the impact of general purpose content extraction methods on the estimation of the readability of a web document. * We propose a bias correction depending on the CE methods which leads to improvements in readability estimation. * We investigate the potential of designing domain specific readability metrics by incorporating web based reference corpora. The rest of the paper is structured as follows: In Section 2 we introduce some of the more common readability formulae.