Combining Hierarchical Clustering and Machine Learning to Predict High-Level Discourse Structure

We propose a novel method to predict the interparagraph discourse structure of text, i.e. to infer which paragraphs are related to each other and form larger segments on a higher level. Our method combines a clustering algorithm with a model of segment "relatedness" acquired in a machine learning step. The model integrates information from a variety of sources, such as word co-occurrence, lexical chains, cue phrases, punctuation, and tense. Our method outperforms an approach that relies on word co-occurrence alone.