Where does Information come from? Corpus Analysis for Automatic Abstracting
Bibliographic record
Abstract
ing Horacio Saggion and Guy Lapalme RALI D#epartement d'Informatique et Recherche Op#erationnelle Universit#e de Montr#eal CP 6128, Succ Centre-Ville Montr#eal, Qu#ebec, Canada, H3C 3J7 Fax: +1-514-343-5834 fsaggion,lapalmeg@iro.umontreal.ca Abstract We report on our study of a corpus of abstracts and parent documents to determinate which structural parts of the parent document are used to extract useful information for an abstract. The results give us a sound basis for automatic abstracting of research articles. Our method for automatic abstracting, called selective analysis, is intended to produce user-oriented abstracts which are indicative in the essential content of the document and informative in the user's interest. 1 Introduction An abstract aims at giving the reader an exact and concise knowledge of the parent document. Abstracts of research articles are produced by their author or by professional abstractors working for abstracting services. In documentary abstrac...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".