Presentations (Communicative Events)

Corpus-Trained Text Generation for Summarization

Kan, Min-yen; McKeown, Kathleen

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.


More About This Work

Academic Units
Computer Science
Proceedings of the Second International Natural Language Generation Conference (INLG 2002)
Published Here
May 10, 2013