  Usage of online textual media is steadily increasing. Daily, more and more
news stories, blog posts and scientific articles are added to the online
volumes. These are all freely accessible and have been employed extensively in
multiple research areas, e.g. automatic text summarization, information
retrieval, information extraction, etc. Meanwhile, online debate forums have
recently become popular, but have remained largely unexplored. For this reason,
there are no sufficient resources of annotated debate data available for
conducting research in this genre. In this paper, we collected and annotated
debate data for an automatic summarization task. Similar to extractive gold
standard summary generation our data contains sentences worthy to include into
a summary. Five human annotators performed this task. Inter-annotator
agreement, based on semantic similarity, is 36% for Cohen's kappa and 48% for
Krippendorff's alpha. Moreover, we also implement an extractive summarization
system for online debates and discuss prominent features for the task of
summarizing online debate data automatically.
