Corpus-Trained Text Generation for Summarization
Min‐Yen Kan, Kathleen R. McKeown · 2002
We explore how machine learning can be employed to learn rulesets for the traditional modules of content planning and surface realization. Our approach takes advantage of semantically annotated corpora to induce preferences for content planning and constraints on realizations of these plans. We applied this methodology to an annotated corpus of indicative summaries to derive constraint rules that can assist in generating summaries for new, unseen material.