Automatic label generation for news comment clusters

Abstract

We present a supervised approach to automatically labelling topic clusters of reader comments to online news. We use a feature set that includes both features capturing properties local to the cluster and features that capture aspects from the news article and from comments outside the cluster. We evaluate the approach in an automatic and a manual, task-based setting. Both evaluations show the approach to outperform a baseline method, which uses tf∗idf to select comment-internal terms for use as topic labels. We illustrate how cluster labels can be used to generate cluster summaries and present two alternative summary formats: a pie chart summary and an abstractive summary.