Bibliographic record
Abstract
Introduction Automatic summarization has traditionally concerned itself with textual documents. Research on that topic began in the late 1950s with the automatic generation of summaries for technical papers and magazine articles (Luhn, 1958). About 30 years later, summarization has advanced into the field of speech-based summarization, working on dialogues (Kameyama et al. , 1996, Reithinger et al. , 2000, Alexandersson, 2003) and multi-party interactions (Zechner, 2001b, Murray et al. , 2005a, Kleinbauer et al. , 2007). Spärck Jones (1993, 1999) argues that the summarizing process can be described as consisting of three steps (Figure 10.1): interpretation (I), transformation (T), generation (G). The interpretation step analyzes the source, i.e., the input that is to be summarized, and derives from it a representation on which the next step, transformation, operates. In the transformation step the source content is condensed to the most relevant points. The final generation step verbalizes the transformation result into a summary document. This model is a high-level view on the summarization process that abstracts away from the details a concrete implementation has to face. We distinguish between two general methods for generating an automatic summary: extractive and abstractive . The extractive approach generates a summary by identifying the most salient parts of the source and concatenating these parts to form the actual summary. For generic summarization, these salient parts are sentences that together convey the gist of the document's content. In that sense, extractive summarization becomes a binary decision process for every sentence from the source: should it be part of the summary or not? The selected sentences together then constitute the extractive summary, with some optional post-processing, such as sentence compression, as the final step.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.079 | 0.057 |
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".