A Map–Summarize Framework for Answer Set Verbalization

We study answer set verbalization: generating readable natural-language descriptions of ASP solver outputs.

Unlike standard data-to-text settings, answer sets often lack an explicit schema and may involve structured, non-relational representations through complex terms rather than flat records.

We propose a modular map–summarize framework that first maps atoms into predicate-aware textual fragments (via schema guidance, LLM-based mapping, or deterministic templates) and then uses a language model primarily for fluent aggregation.

We evaluate the framework through a quantitative benchmark on structured ASP request databases, and a human evaluation of solver-generated explanation graphs.

Results show that predicate-aware structured methods improve faithfulness and efficiency over direct LLM prompting.