Subjective extractive summarization is critical for systems that help users digest large corpora—news, reports, and research, where what “matters” is refined over time. SuDocu is an existing subjective summarization system that captures the user's summarization intent as a constrained optimization problem (integer linear program) using user-created example summaries. The problem is then solved to produce the final summary with the optimal set of sentences. This approach is feasible for just one summary generation, but in reality, users refine their intent iteratively until they are satisfied. Requiring a new set of examples for every refinement is slow, cognitively heavy, and assumes the user already understands the latent topic structure of the corpus. Our goal is to extend SuDocu to support conversational, multi-turn intent refinement, enabling interactive, exploratory “what-if” summaries without requiring new example clicks.

We introduce an extension to the existing SuDocu infrastructure that we call SuDocu+, which will enable conversational capabilities for the user, through a lightweight natural-language refinement channel mapping a user's conversational commands (e.g., “more nature, less politics”, “emphasize recent developments”) into small auditable updates of package-query bounds and objective weights, explaining changes and flagging infeasibility if needed. In addition, we will contribute to warm-started, progressive optimization that reuses the previous solution, delivering fast updates with improvement guarantees and full provenance. At the center of SuDocu+ will be an intent-to-constraint mapping module that translates natural-language refinement into concrete adjustments to the underlying package query. This preserves the prescriptive guarantees of package-query optimization while enabling low-effort, iterative refinement in natural language.