Domain-independent text segmentation using anisotropic diffusion and dynamic programming

This paper presents a novel domain-independent text segmentation method, which identifies the boundaries of topic changes in long text documents and/or text streams. The method consists of three components: As a preprocessing step, we eliminate the document-dependent stop words as well as the generic stop words before the sentence similarity is computed. This step assists in the discrimination of the sentence semantic information. Then the cohesion information of sentences in a document or a text stream is captured with a sentence-distance matrix with each entry corresponding to the similarity between a sentence pair. The distance matrix can be represented with a gray-scale image. Thus, a text segmentation problem is converted into an image segmentation problem. We apply the anisotropic diffusion technique to the image representation of the distance matrix to enhance the semantic cohesion of sentence topical groups as well as sharpen topical boundaries. At last, the dynamic programming technique is adapted to find the optimal topical boundaries and provide a zoom-in and zoom-out mechanism for topics access by segmenting text in variable numbers of sentence topical groups. Our approach involves no domain-specific training, and it can be applied to texts in a variety of domains. The experimental results show that our approach is effective in text segmentation and outperforms several state-of-the-art methods.