Work

A Map–Summarize Framework for Answer Set Verbalization

Year: 2026

Type: conference-paper

Abstract: 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.

Source: Principles of Knowledge Representation and Reasoning

DOI: 10.24963/kr.2026/75

Authors: Mario Alviano, Matteo Capalbo, Sebastiano A. Piccolo

Institution: University of Calabria

Language: English

Topic: Logic, Reasoning, and Knowledge

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze