Supporting knowledge discovery through visual means is a hot research topic in the field of visual analytics in general, and a key issue in the analysis of textual data sets. In that context, the StorylineViz study aims at developing a generic approach to narrative analysis, supporting the identification of significant patterns inside textual data, and ultimately knowledge discovery and sensemaking. It builds on a text segmentation procedure through which sequences of situations are extracted. A situation is defined by a quadruplet of components: actors, space, time and motion. The approach aims at facilitating visual reasoning on the structure, rhythm, patterns and variations of heterogeneous texts in order to enable comparative analysis, and to summarise how the space/time/actors/motion components are organised inside a given narrative. It encompasses issues that are rooted in Information Sciences - visual analytics, knowledge representation – and issues that more closely relate to Digital Humanities – comparative methods and analytical reasoning on textual content, support in teaching and learning, cultural mediation.