Abstractive Summarization of Spoken and Written Conversations Based on Phrasal Queries
Why this work is in the frame
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Bibliographic record
Abstract
We propose a novel abstractive querybased summarization system for conversations, where queries are defined as phrases reflecting a user information needs. We rank and extract the utterances in a conversation based on the overall content and the phrasal query information. We cluster the selected sentences based on their lexical similarity and aggregate the sentences in each cluster by means of a word graph model. We propose a ranking strategy to select the best path in the constructed graph as a query-based abstract sentence for each cluster. A resulting summary consists of abstractive sentences representing the phrasal query information and the overall content of the conversation. Automatic and manual evaluation results over meeting, chat and email conversations show that our approach significantly outperforms baselines and previous extractive models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it