The Italica System at TAC 2008 Opinion Summarization Task
Bibliographic record
Abstract
texts. Second, the goal is not to obtain asummary of the complete documents, but the answersgiven in the documents to several questions. As inputto the system, apart from the documents themselves,the participants are provided with the questions andalso the snippets that answer those questions, beingthe latter generated by the QA systems participatingin another TAC 2008 task.Oursystemisbasedonthecombinationofthesnip-pets provided for the summary construction. In orderto make the text more readable and complete the in-formation, the process starts looking for the most re-levant sentences in relation to the snippets, which arethen used to generate the text that will flnally com-pose the summary. Our system is therefore focusedon the sentence extraction phase. We have obtainedgood results with the pyramid F-score and overall res-ponsiveness measures, achieving the second and flrstplace respectively among the participating systems.In the following sections we describe the architec-ture of the system in flrst place, and continue dis-cussing the results obtained in the evaluation process.Finally, we conclude discussing the strong and weakpoints of our system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.030 |
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".