Information Retrieval (IR) typically retrieves entire documents in response to a user\u27s information need. However, many times a user would prefer to examine smaller portions of a document. One example of this is when building a frame-based representation of a text. The user would like to read all and only those portions of the text that are about predefined important features. This research addresses the problem of automatically locating text about these features, where the important features are those defined for use by a case-based reasoning (CBR) system in the form of features and values or slots and fillers. To locate important text pieces we gathered a small set of excerpts , textual segments, when creating the original case-base representations. Each segment contains the local context for a particular feature within a document. We used these excerpts to generate queries that retrieve relevant passages. By locating passages for display to the user, we winnow a text down to sets of several sentences, greatly reducing the time and effort expended searching through each text for important features
