Demonstrating Domain-informed Summarization through Contrast (DiSCo)

Intelligent interfaces increasingly rely on large language models (LLMs) to summarize user-generated content. While efective at condensing what is explicitly mentioned, known as presence bias, such summaries systematically overlook what is expected yet missing. This presence bias can mislead users by obscuring diagnostic gaps that matter for decision making. We present a demonstration of DiSCo (Domain-informed Summarization through Contrast), a method for review summarization that explicitly surfaces both salient and absent information. The system constructs domain-level topical expectations from comparable entities and contrasts them with individual review profles to identify over-represented and missing aspects. These contrasts are then integrated into summaries using LLMs, allowing users to explore what is present and stands out and what is missing compared to other entities in the domain. The demo showcases how DiSCo summaries change user interaction with review content across multiple accommodation domains. Participants can interactively toggle expectation views, inspect missing aspects, and compare standard LLM summaries with DiSCo summaries in real time. The demonstration illustrates how expectation-aware interfaces support transparency, refection, and better-informed decision making.