Analyzing Multi-Sentence Aggregation in Abstractive Summarization via the Shapley Value
Bibliographic record
Abstract
Abstractive summarization systems aim to write concise summaries capturing the most essential information of the input document in their own words.One of the ways to achieve this is to gather and combine multiple pieces of information from the source document, a process we call aggregation.Despite its importance, the extent to which both reference summaries in benchmark datasets and systemgenerated summaries require aggregation is yet unknown.In this work, we propose AG-GSHAP, a measure of the degree of aggregation in a summary sentence.We show that AGGSHAP distinguishes multi-sentence aggregation from single-sentence extraction or paraphrasing through automatic and human evaluations.We find that few reference or modelgenerated summary sentences have a high degree of aggregation measured by the proposed metric.We also demonstrate negative correlations between AGGSHAP and other quality scores of system summaries.These findings suggest the need to develop new tasks and datasets to encourage multi-sentence aggregation in summarization.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".