In this tutorial, we will review the state of the art in automatic summarization, and will discuss and critically evaluate current approaches to the problem. We will describe the typical decomposition of summarization into three stages, and explain in detail the major approaches to each stage. For topic identification, we will outline techniques based on stereotypical text structure, cue words, high-frequency indicator phrases, intratext connectivity, and discourse structure centrality. For topic fusion, we will outline some ideas that have been proposed, including concept generalization and semantic association. For summary generation, we will describe the problems of sentence planning to achieve information compaction.
Evaluation is a difficult issue.
We will describe various suggested measures and discuss the adequacy of current evaluation methods. Whenever possible, we will make explicit the relationships between computational and psycholinguistic research on summarization. Finally, we will present a set of open problems that we perceive as being crucial for immediate progress in automatic summarization. Tutorial notes PostScript, Compressed, PowerPoint Eduard Hovy directs the Natural Language Group at the Information Sciences Institute of the University of Southern California. Other related work he has been or is currently involved with include concept clustering, large ontology construction, machine translation, and text generation.