CCNU at TAC 2008:Proceeding on Using Semantic Method for Automated Summarization Yield
Bibliographic record
Abstract
The CCNU summarization system, PUSMS (Proceeding to Using Semantic Method for Summarization), join in TAC (formerly DUC) for the first time. For the update summarization tasks, we used syntacticbased anaphora resolution and sentence compression algorithms in our system. Term significance was then obtained by frequency-related topic significance and query-related significance by obtaining cooccurrence information with query terms. For the pilot QA summarization task, a semantic orientation recognition module which used WordNet::Similarity::Vector to obtain all of the main part-of-speech terms’ similarity with benchmark words derived from General Inquirer is used in PUSMS pilot system. We also developed a document classifier and a snippets-related content extracting module for the pilot tasks. In all, our initial job can be boiled down to be introducing semantic method into our former statistical summarization system. By analyzing the evaluation results, we found that we were preceding the right target but still have a long way to go.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".