Research on Graph-based Text Summarization Extraction Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a graph-based text summarization extraction algorithm. The algorithm is based on directed graphs and can incorporate the position information of sentences into the computational scope. When calculating the edge weights of nodes in the directed graph, a pre-trained model after negative sampling is used, which not only can extract deeper semantic features but also enable higher relevance between the contextual sentences in the article. The algorithm also introduces a weighting mechanism to adjust the extraction priority of the sentences according to the article's theme, resulting in a higher quality of extracted summary sentences that can represent the key information of the text as much as possible. The algorithm can capture the key information in the text, reduce the impact of irrelevant information on semantics, and play a role in text compression.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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