Abstract
Selecting a subset of linguistic summaries and providing them in a user-friendly and compact form is a latent issue in the field of Linguistic Data Summarization. The paper proposes a method for filtering the most useful subset, for a given decision problem, from a set of composite linguistic summaries. Those summaries embody Evidence, Contrast or Emphasis relations, inspired by the Rhetorical Structure Theory. The summaries’ usefulness is determined according to the relevance of the attributes contained in each one. The strategy followed is based on first finding the Evidence relation whose nucleus contains the better possible representation of the problem attributes, then searching for a Contrast relation and an Emphasis relation that share that nucleus. The method output is a scheme that synthesizes and combines the texts of the three relations. The paper provides an illustrative example in which the most useful relations are found from a dataset of 63 crimes to solve a case of bank document forgery.
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Rodríguez Rodríguez, C.R., Peña Abreu, M., Zuev, D.S., Amoroso Fernández, Y., Zulueta Véliz, Y. (2024). A Novel Method for Filtering a Useful Subset of Composite Linguistic Summaries. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2023. Lecture Notes in Computer Science, vol 14335. Springer, Cham. https://doi.org/10.1007/978-3-031-49552-6_16
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