On Table Extraction from Text Sources with Markups

Table extraction is the task of locating tables in a document and extracting their content along with its arrangement within the tables. The notion of tables applied in this work excludes any sort of meta data, e.g. only the entries of the tables are to be extracted. We follow a simple unsupervised approach by selecting the tables according to a score that measures the in-column consistency as pairwise similarities of entries where separa-tor columns are also taken into account. Since the average similarity is less reliable for smaller tables this score demands a levelling in favor of greater tables for which we make different propo-sitions that are covered by experiments on a test set of HTML documents. In order to reduce the number of candidate tables we use assumptions on the entry borders in terms of markup tags. They only hold for a part of the test set but al-low us to evaluate any potential table without re-ferring to the HTML syntax. The experiments indicate that the discriminative power of the in-column similarities is limited but also consider-able given the simplicity of the applied similarity functions.