VENCE: A new machine learning method enhanced by ontological knowledge to extract summaries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Obtaining extractive summaries by using functions induced from a training set continue to be a great challenge in the domain of the automatic text summary. This paper presents the VENCE method based on this approach and improves the quality of the abduced functions, using semantic relations of the words (attributes) of the training set that are fetched from a ontology to be inserted in this set. The choice of this training set is reinforced with the optimization of the space of attributes by means of statistical techniques, as well as with the introduction of the Jaccard index, calculated from considering a manual summary that is extracted from the corpus of the chosen documents. The VENCE method is explained in details as well as the different experiments conducted to propose an optimal process. Its application to a text document corpus highlighted its efficiency. The results obtained are very satisfactory for the assessment of discriminating power of the abduced classification function as well as for the quality of summaries produced.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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