CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics

Measuring the quality of public deliberation requires evaluating not only civility or argument structure, but also the informational progress of a conversation. We introduce a framework for Conversational Information Gain (CIG) that evaluates each utterance in terms of how it advances collective understanding of the target topic. To operationalize CIG, we model an evolving semantic memory of the discussion: the system extracts atomic claims from utterances and incrementally consolidates them into a structured memory state. Using this memory, we score each utterance along three interpretable dimensions: Novelty, Relevance, and Implication Scope. We annotate 80 segments from two moderated deliberative settings (TV debates and community discussions) with these dimensions and show that memory-derived dynamics (e.g., the number of claim updates) correlate more strongly with human-perceived CIG than traditional heuristics such as utterance length or TF-IDF. We develop effective LLM-based CIG predictors paving the way for information-focused conversation quality analysis in dialogues and deliberative success. 1 Aspect Label Definition / Anchor Conversational Information Gain 1. No gain Repeats or obstructs; no meaningful advance beyond the existing knowledge. 2. Minimal gain Small clarification or slight nuance that is noticeable but limited. 3. Incremental Adds new details/mechanisms within the same conceptual frame or ideas within the topic. 4. Insightful Reframes or introduce new ideas under the topic; shifts the conversation in a new valuable way. Novelty 1. Not novel Repetition/paraphrase of prior content or non/common-sense content. 2. Minimally novel Minor or mostly predictable detail added to an existing idea. 3. Moderately novel New evidence, concrete example, or supporting detail expanding an existing idea. 4. Highly novel New framework, principle, idea, or line of reasoning that opens a new direction. Relevance 1. Not relevant Off-topic; no connection to the conversation goal. 2. Minimally relevant Loose or indirect link; requires inference to connect. 3. Moderately relevant Substantially related but not central (e.g., side issue or counterpoint). 4. Highly relevant Directly and explicitly addresses the core topic or goal.