LESS is More: LEan Computing for Selective Summaries

An agent in pursuit of a task may work with a corpus containing text documents. To perform information retrieval on the corpus, the agent may need annotations-additional data associated with the documents. Subjective Content Descriptions (SCDs) provide additional location-specific data for text documents. SCDs can be estimated without additional supervision for any corpus of text documents. However, the estimated SCDs lack meaningful descriptions, i.e., labels consisting of short summaries. Labels are important to identify relevant SCDs and documents by the agent and its users. Therefore, this paper presents LESS, a LEan computing approach for Selective Summaries, which can be used as labels for SCDs. LESS uses word distributions of the SCDs to compute labels. In an evaluation, we compare the labels computed by LESS with labels computed by large language models and show that LESS computes similar labels but requires less data and computational power.