Advances in domain independent linear text segmentation

TL;DR

This paper introduces a new linear text segmentation method that significantly improves accuracy and speed by using rank-based similarity and divisive clustering for boundary detection.

Contribution

The paper presents a novel approach to text segmentation that outperforms previous methods in both accuracy and computational efficiency.

Findings

Twice as accurate as previous methods

Over seven times faster in processing

Effective boundary detection via divisive clustering

Abstract

This paper describes a method for linear text segmentation which is twice as accurate and over seven times as fast as the state-of-the-art (Reynar, 1998). Inter-sentence similarity is replaced by rank in the local context. Boundary locations are discovered by divisive clustering.