Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval
Bibliographic record
Abstract
Multi-document summarization (MDS) assumes a set of topic-related documents are provided as input.In practice, this document set is not always available; it would need to be retrieved given an information need, i.e. a question or topic statement, a setting we dub "opendomain" MDS.We study this more challenging setting by formalizing the task and bootstrapping it using existing datasets, retrievers and summarizers.Via extensive automatic and human evaluation, we determine: (1) state-of-theart summarizers suffer large reductions in performance when applied to open-domain MDS, (2) additional training in the open-domain setting can reduce this sensitivity to imperfect retrieval, and (3) summarizers are insensitive to the retrieval of duplicate documents and the order of retrieved documents, but highly sensitive to other errors, like the retrieval of irrelevant documents.Based on our results, we provide practical guidelines to enable future work on open-domain MDS, e.g.how to choose the number of retrieved documents to summarize.Our results suggest that new retrieval and summarization methods and annotated resources for training and evaluation are necessary for further progress in the open-domain setting. 1 How to assemble the document index?For our purposes, we take the set of all documents in the train, validation, and test splits of each dataset to form D index .This guarantees that the ground-truth documents for each example are present in the index while providing plenty of negative examples.How many documents to summarize?The number of retrieved documents to summarize, k, 6 See the BEIR (Thakur et al., 2021) zero-shot benchmark 7 Except for MSˆ2, where we found the provided "background" section to perform better as a query; see Appendix A 8 We also experimented with query generation using LLMs (e.g.GPT-3), but found that they significantly underperformed the reference summary as query, e.g. by at least 8 points P/R@K on a sample of the Multi-News validation set
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".