Interleaved Evaluation for Retrospective Summarization and Prospective Notification on Document Streams
Bibliographic record
Abstract
We propose and validate a novel interleaved evaluation methodology for two complementary information seeking tasks on document streams: retrospective summarization and prospective notification. In the first, the user desires relevant and non-redundant documents that capture important aspects of an information need. In the second, the user wishes to receive timely, relevant, and non-redundant update notifications for a standing information need. Despite superficial similarities, interleaved evaluation methods for web ranking cannot be directly applied to these tasks; for example, existing techniques do not account for temporality or redundancy. Our proposed evaluation methodology consists of two components: a temporal interleaving strategy and a heuristic for credit assignment to handle redundancy. By simulating user interactions with interleaved results on submitted runs to the TREC 2014 tweet timeline generation (TTG) task and the TREC 2015 real-time filtering task, we demonstrate that our methodology yields system comparisons that accurately match the result of batch evaluations. Analysis further reveals weaknesses in current batch evaluation methodologies to suggest future directions for research.
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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 | 0.020 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".