People browsing the web or reading a document may see text passages that describe a topic of interest, and want to know more about it by searching. Manually formulating a query from that text can be difficult, however, and an effec-tive search is not guaranteed. In this paper, to address this scenario, we propose a learning-based approach which gener-ates effective queries from the content of an arbitrary user-selected text passage. Specifically, the approach extracts and selects representative chunks (noun phrases or named entities) from the content (a text passage) using a rich set of features. We carry out experiments showing that the se-lected chunks can be effectively used to generate queries both in a TREC environment, where weights and query structure can be directly incorporated, and with a “black-box ” web search engine, where query structure is more limited
