Discrepancy Between Automatic and Manual Evaluation of Summaries
Bibliographic record
Abstract
Today, automatic evaluation metrics such as ROUGE have become the de-facto mode of evaluating an automatic summarization system. However, based on the DUC and the TAC evaluation results, (Conroy and Schlesinger, 2008; Dang and Owczarzak, 2008) showed that the performance gap between humangenerated summaries and system-generated summaries is clearly visible in manual evaluations but is often not reflected in automated evaluations using ROUGE scores. In this paper, we present our own experiments in comparing the results of manual evaluations versus automatic evaluations using our own text summarizer: BlogSum. We have evaluated BlogSum-generated summary content using ROUGE and compared the results with the original candidate list (OList). The t-test results showed that there is no significant difference between BlogSum-generated summaries and OList summaries. However, two manual evaluations for content using two different datasets show that BlogSum performed significantly better than OList. A manual evaluation of summary coherence also shows that Blog-Sum performs significantly better than OList. These results agree with previous work and show the need for a better automated summary evaluation metric rather than the standard ROUGE metric. 1
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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 | 0.030 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".