Ponceleon,D.: Hierarchical segmentation: finding changes in a text signal,In

Abstract

This paper describes a signal processing algorithm which discovers the hierarchical organization of a document or media presentation. We use latent semantic indexing to describe the semantic content of the signal, and scalespace segmentation to describe its features at many different scales. We represent the semantic content of the document as a signal that varies through the document. We lowpass filter this signal to compute the document’s semantic path at many different time scales and then look for changes. The changes are sorted by their strength to form a hierarchical segmentation. We present results from a text document and a video transcript. 1. THE PROBLEM As prices decline and storage and computational horsepower increase, we will soon be swamped in multimedia data. Unfortunately, given an audio or a video signal there is little information readily available that can help us find our way around such a time-based signal. Technical papers are structured into major and minor headings, imposing a hierarchical structure. Often professional or high-quality audio–visual (AV) presentations are also structured. However, this information is hidden in the signal. Our goal is to use the semantic information in the AV signal to create a hierarchical table of contents that describes the associated signal. Towards this end we combine two powerful concepts: scale space (SS) filtering and Latent Semantic Indexing (LSI). We use LSI to provide a continuously valued feature that describes the semantic content of an AV signal. By doing this we reduce the dimensionality of the problem and, more importantly, we address synonymy and poly