Summary writing strategies based on discourse structures, relevance theory and kernel preservation
Bibliographic record
Abstract
This paper explores methods of guiding summary writers based on the dis-course structure of the material being summarized. Multi-item discourse strudures are identified, and the problem-solution macrostrudures (of special interest for technical writing) are used as an example of how we can preserve the ''gist" (content plus organization) of the original message in such genres. A similar approach is taken for the established binary logical relations of discourse connection, which can form the basis of text macrostructure or microstructure. Descriptive texts do not have such macrostructures on which to base summaries. For these, the advice provided consists in selecting high-priority items of information based on adaptations to relevance theory. Most texts are recognized as having many structural levels, with different types of structure at each level. The approach adopted for summarizing such texts is progressive summarization of the text strata using the methods appropriate for the structure at each level. The need to identify and retain the central "kernel" or "essence" of the original material, as well as the gist, is explained and demonstrated. This high-lights the need for skilled judgment in selecting vital material for inclusion in the summary -something computer summarizing tools cannot yet accomplish. Deletion heuristics for summarizing are provided based on established text structures.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 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".