textsmost
Textsmost is a term used in text analysis to describe the process or outcome of selecting the most informative subset of texts from a larger corpus, according to a predefined criterion of usefulness. It refers to a family of techniques aimed at identifying representative, diverse, or topic-covered texts rather than all items in the collection.
Formally, given a corpus C and an integer k, the textsmost problem seeks a subset S ⊆ C
Applications for textsmost include curating training datasets for natural language processing models, constructing concise document summaries,
Limitations and considerations include dependence on the chosen utility function and similarity metrics, potential biases inherited
See also: submodular optimization, text summarization, data curation, corpus representativeness.