An Ontology-based Visual Interface for Browsing and Summarizing Conversations
Bibliographic record
Abstract
In this paper we present a visual interactive interface to cre-ate focused summaries of human conversations via mapping to the concepts within an ontology. The ontology includes nodes for the conversation participants, for Dialog Act (DA) properties such as decision, action-item or subjectivity, as well as for entities mentioned in the conversation. The clas-sifiers used to annotate conversation data with DA property and entity tags can be applied to any conversational modal-ity including face to face meetings, emails, blogs and chats. Our interface allows the user to explore these conversations and identify informative sentences by their association with nodes of interest on the tree-structured visual representation of the ontology. The sentences thus selected by the user as potentially important components of the summary can then be used to derive a brief and focused overview of the conver-sation. The display interface and data parsing components in the initial prototype were all developed based on Java frame-works and toolkits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".