Summary and Distance between Sets of Texts based on Topological Data Analysis
This provides a novel approach for literary analysis and text comparison, though it is incremental as it integrates existing techniques in a new way.
The paper tackles the problem of summarizing and comparing sets of texts by combining topological data analysis tools like persistent homology and bottleneck distance with deep-learning word embeddings, resulting in a method applied to Spanish Golden Age poets to characterize and distance literary styles.
In this paper, we use topological data analysis (TDA) tools such as persistent homology, persistent entropy and bottleneck distance, to provide a {\it TDA-based summary} of any given set of texts and a general method for computing a distance between any two literary styles, authors or periods. To this aim, deep-learning word-embedding techniques are combined with these tools in order to study the topological properties of texts embedded in a metric space. As a case of study, we use the written texts of three poets of the Spanish Golden Age: Francisco de Quevedo, Luis de Góngora and Lope de Vega. As far as we know, this is the first time that word embedding, bottleneck distance, persistent homology and persistent entropy are used together to characterize texts and to compare different literary styles.