ClassStruggle: a clustering based text segmentation

Sylvain Lamprier, Tassadit Amghar, Bernard Levrat, Frédéric Saubion

Abstract: This paper describes ClassStruggle, an algorithm for linear text segmentation on general corpuses. It relies on an initial clustering of the sentences of the text. This preliminary partitioning provides a global view on the sentences relations existing in the text, considering the similarities in a group rather than individually. ClassStruggle is based on the distribution of the occurrences of the members of each class. During the process, the clusters then evolve, by considering a notion of proximity and of layout in the text, in the aim to create groups that contain only sentences related to a same topic development. Finally, boundaries are created between sentences belonging to two different classes. First experimental results are promising, ClassStruggle appears to be very competitive compared with existing methods.