Lookahead Summary Coverage: Predicting Which Source Details to Retain in a Summary

Automatic summarization requires a system to identify which source details should be retained, compressed, combined, or omitted while preserving the information necessary to understand the source. Conventional summarization systems commonly rely on learned representations, salience estimation, extractive selection, or abstractive generation. However, the importance of a source detail depends not only on its immediate relevance but also on the information that would be lost if it were excluded and the redundancy or coverage effects of retaining it alongside other details. This paper proposes Lookahead Summary Coverage (LSC), a conceptual framework that predicts the downstream effects of alternative source-detail selection decisions before constructing the final summary. Given a source document, LSC identifies candidate information units, estimates their semantic importance and redundancy, predicts the coverage and factual consequences of retaining or omitting them, and selects a subset or compressed representation under a length budget. The framework formulates summary construction as a sequential decision problem in which the marginal value of a detail depends on the information already selected, the remaining budget, and the expected content of the final summary. A lookahead evaluator estimates the effects of alternative decisions on information coverage, factual fidelity, coherence, redundancy, and summary length. An uncertainty-aware selection mechanism combines these estimates with computational cost, while an optional learning component improves predictions from reference summaries and human evaluations. The paper presents a mathematical formulation, a proposed architecture, an inference algorithm, and an experimental protocol for comparison with extractive, abstractive, and coverage-aware summarization methods. No experimental results are claimed. The proposed framework should be evaluated to determine whether explicit prediction of downstream information loss improves summary quality under comparable length and computational constraints.