Selective analysis for automatic abstracting: evaluating indicativeness and acceptability
Bibliographic record
Abstract
ing: Evaluating Indicativeness and Acceptability Horacio Saggion and Guy Lapalme Departement d'Informatique et Recherche Operationnelle Universite de Montreal CP 6128, Succ Centre-Ville Montreal, Quebec, Canada, H3C 3J7 Fax: +1-514-343-5834 {saggion,lapalme}@iro.umontreal.ca Abstract We have developed a new methodology for automatic abstracting of scientific and technical articles called Selective Analysis. This methodology allows the generation of indicativeinformative abstracts integrating different types of information extracted from the source text. The indicative part of the abstract identifies the topics of the document while the informative one elaborates some topics according to the reader's interest. The first evaluation of our methodology demonstrates that Selective Analysis performs well in the task of signaling the topic of the document demonstrating the viability of such a technique. The sentences the system produces from instantiated templates are conside...
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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 | 0.012 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".