Towards Answer-Focused Summarization

_ People query search engines to find answers to a variety of questions on the Internet. Search cost would have been greatly reduced if search engines could accept natural language questions as queries, and provide summaries that contain the answers to these questions. We introduce the notion of Answer-Focused Summarization, which is to combine summarization and question answering. We develop a set of criteria and performance metrics, to evaluate answerFocused Summarization. We demonstrate that the summaries produced by Google, the most popular search engine nowadays, can be largely improved for question answering. We develop a proximity-based summary extraction system, and then utilize question types, i.e. whether the question is a "person" or a "place" question, to improve the performance. We suggest that there is a large application potential for Answer-Focused Summarization, such as in wireless and palmheld systems where search cost is critical.