Improving Extractive Text Summarization Performance Using Enhanced Feature Based RBM Method
Bibliographic record
Abstract
Text summarization is the process of creating a short, accurate and fluent summary of a longer text document. As plenty of digital data is available online, automatic text summarization methods greatly needed to help and understand the lengthy & complex documents quickly by discovering the relevant information. This paper proposes the text summarization method for short news articles and long scientific papers using unsupervised neural network model. The proposed method works in four main steps: input document pre-processing, feature extraction, feature enhancement and final summary generation. We have extracted combination of various statistical and linguistic features from input document, which helps in improving the quality of sentence selection. Further Restricted Boltzmann Machine (RBM) model is used to capture & enhance the discriminative, abstract features in an unsupervised way to improve the overall performance without losing any significant information. Sentences are scored based on enhanced feature set and top sentences are selected for final extractive summary. Performance of the proposed method is evaluated using Rouge score and compared with TextRank, LexRank, LSA & Luhn baseline methods and the results demonstrates that proposed methodology performs better compared to other methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".