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 with size |S| = k that maximizes a utility function U(S). Utility commonly balances coverage of topics, linguistic diversity, and minimization of redundancy among selected texts. Many formulations model U as a submodular function, enabling efficient approximate optimization via a greedy algorithm with a near-optimal bound (roughly 1 − 1/e). Preprocessing steps typically include text normalization, vectorization (such as TF-IDF or contextual embeddings), and the construction of a similarity or feature space to evaluate coverage and redundancy.

Applications for textsmost include curating training datasets for natural language processing models, constructing concise document summaries, and creating representative benchmarks from large document collections. In practice, the approach helps reduce data volume while preserving essential content and diversity, facilitating faster experimentation and analysis.

Limitations and considerations include dependence on the chosen utility function and similarity metrics, potential biases inherited from the source corpus, computational cost for very large datasets, and the risk that niche topics may be underrepresented without careful tuning. As a flexible framework, textsmost can be adapted through domain-specific features, similarity measures, or constraint sets to suit particular research or deployment goals.

See also: submodular optimization, text summarization, data curation, corpus representativeness.